Antarctic Station-Based Pressure Reconstructions from 1905-2011 Using Principal

Home , Bellingshausen Sea, Bellingshausen Station

Antarctic Station-based Pressure Reconstructions from 1905-2011 using Principal

Component Regression

A thesis presented to

the faculty of

the College of Arts and Sciences of Ohio University

In partial fulfillment

of the requirements for the degree

Master of Science

Ming Yeung Lee

May 2013

This thesis titled

Antarctic Station-based Pressure Reconstructions from 1905-2011 using Principal

Component Regression

MING YEUNG LEE

has been approved for

the Department of Geography

and the College of Arts and Sciences by

Ryan L. Fogt

Assistant Professor of Meteorology

Robert Frank

Dean, College of Arts and Sciences 3

ABSTRACT

LEE, MING YEUNG, M.S., May 2013, Geography

Antarctic Station-based Pressure Reconstructions from 1905-2011 using Principal

Component Regression

Director of Thesis: Ryan L. Fogt

In Antarctica, most weather stations did not start collecting meteorological observations until the late 1950s. To extend these records and provide a more complete picture of the pressure variability at each station, this thesis reconstructs the pressure records at 18 different Antarctic stations back to 1905, based on the Principal Component

Regression (PCR). The PCR model uses only Southern Hemisphere mid-latitude pressure observations used as predictors. Several independent validation techniques are used to examine the level of accuracy of the PCR models, such as calibration correlation, validation correlation, reduction of error (RE), coefficient of efficiency (CE), and a comparison of the observed and reconstructed trends (1957-2011). The results have shown that austral summer and austral winter have higher reconstruction skill, whereas austral fall and austral spring are slightly weaker. Nevertheless, the reconstructions of all seasons are still better than using the climatological mean alone, and as such, all reconstructions are deemed to have some scientific use.

Since the reconstructions extend nearly double the observed record at each station and provide a more thorough depiction of the scope of pressure variability at each site, the uniqueness of changes throughout the historical record (1905-2011) can be examined.

To accomplish this, several methods were used, including a 7-/ 11-year smoothing, 4 comparison of decadal means, and comparison of trends, using both fixed (1905-1956 vs.

1957-2011) and temporally varying time periods (30-/40-/50-year running trends). In austral summer and autumn, a significant (p<0.05) decreasing trend can be seen in East

Antarctica from 1990-2010, with these decades recording the lowest station pressures over the last century. These dramatic changes are believed to be related to stratospheric ozone depletion and/or greenhouse gases, especially in austral summer. In austral winter and spring, a shift to a most positive trend is observed from 1970-2011 in West

Antarctica (particularly the Antarctic Peninsula). The recent (1957-2011) positive trends along the Antarctic Peninsula in austral spring are statistically significant (p<0.05).

Interestingly, the lowest decadal mean pressures occurred in East Antarctica during the

1930s in austral spring, autumn, and winter. Although these reconstructions highlight many unique events since 1905, future research is still required to understand the mechanisms that influence many of these trends and fluctuations.

In honor of my parents, KiWai and TaiTai,

and my sister, SoTing,

for their love and support.

ACKNOWLEDGMENTS

I would like to express my deepest gratitude to my advisor, Dr. Ryan L. Fogt, for his patience, understanding, and most importantly, his friendship during my graduate studies at Ohio University. I am very grateful and honored to have been his research assistant throughout his project. His mentorship was paramount in providing a well- rounded experience that has shaped me to become a better researcher. He graciously donated his time and effort to revise my thesis and has provided valuable guidance over the last two years. For everything that you have done for me, Dr. Fogt, I thank you.

I would also like to extend my sincere thanks to my other committee members,

Dr. Timothy G. Anderson and Dr. Dorothy Sack, for being encouraging, supportive, and giving helpful comments. I would also like to thank the Department of Geography for accepting me into the graduate program and providing a level of assistance that I cannot put into words, educationally and financially.

I am also grateful for those friends and peers that I have met through my graduate study. Being a foreign student in the United States has definitely been a fruitful and superb experience that I will never forget. Finally, a special thanks to my family, who has provided me with endless encouragement and love. I would not be where I am today without them.

TABLE OF CONTENTS

Page

Abstract ...... 3 Dedication ...... 5 Acknowledgments...... 6 List of Tables ...... 9 List of Figures ...... 10 Chapter 1: Introduction ...... 13 Chapter 2: Literature Review...... 20 2.1 Temperature trends in Antarctica ...... 20 2.2 Atmospheric circulation in Antarctica ...... 26 2.2.1 Southern Annular Mode (SAM) ...... 28 2.2.2 El Niño – Southern Oscillation (ENSO) ...... 30 2.3 Anthropogenic forcing influencing Global Climate ...... 32 2.3.1 Ozone ...... 32 2.3.2 Greenhouse gases ...... 37 2.3.3 Impact of ozone and greenhouse gases ...... 39 2.4 Chapter Summary ...... 41 Chapter 3: Data and Methods ...... 43 3.1 Data ...... 43 3.1.1 South Hemisphere Mid-latitude Stations ...... 43 3.1.2 Antarctic stations ...... 45 3.2 Methodology ...... 48 3.2.1 Correlation analysis ...... 49 3.2.2 Principal Component Regression (PCR) ...... 50 3.2.3 Trend analysis ...... 52 3.3 Validation Methods ...... 53 3.3.1 Leave-one-out Cross Validation (Calibration and Validation period 1957- 2011) 55 3.3.2 30-year Calibration Period and 25-year Validation Period ...... 56 Chapter 4: Pressure Reconstructions ...... 58 8

4.1 Overall pressure reconstruction result ...... 58 4.2 Trend validation ...... 79 4.3 Station-based pressure reconstruction ...... 84 4.3.1 High-skill pressure reconstruction stations ...... 84 4.3.2 Low-skill pressure reconstruction stations...... 95 4.3.3 Validation sensitivity test performance ...... 104 4.4 Chapter Summary ...... 106 Chapter 5: Significant Events in 1905-2011 ...... 113 5.1 Low-frequency variability from the reconstructed records ...... 113 5.1.1 Decadal comparison ...... 114 5.1.2 7-year/ 11-year smoothed low-pass triangular filters ...... 119 5.2 Historical station pressure variability and trends ...... 126 5.2.1 Two period trends comparison ...... 126 5.2.2 30-/40-/50-year trends ...... 131 5.3 Chapter Summary ...... 137 Chapter 6: Summary and Conclusions ...... 142 References ...... 150 Appendix: Station Pressure Reconstructions in 5% and 10% Predictor Networks in All Seasons ...... 157

LIST OF TABLES Page

Table 2.1: Antarctic Peninsula stations‟ annual mean air temperatures and trends with 95% confidence interval...... 24 Table 3.1: Antarctic stations used in the point-based reconstructions...... 47 Table 3.2: The total number of predictors in each station (both p<0.05 and p<0.10 networks in each season) that are used in the PCR models...... 48 Table 4.1: Number of stations where correlations between the reconstruction and observed values are larger than 0.6 and 0.8 respectively, during the calibration period. . 60 Table 4.2: Number of stations where correlations between the reconstruction and observed values are larger than 0.4, 0.6 and 0.8 respectively, during the validation period...... 65 Table 4.3: Number of stations where reduction of error (RE) is larger than 0.2, 0.4, and 0.6, respectively...... 70 Table 4.4: Number of stations where coefficient of efficiency (CE) is larger than 0.2, 0.4 and 0.6, respectively...... 75 Table 4.5: Validation correlation differences for a) summer, b) autumn, c) winter, and d) spring in the full period tests and the validation sensitivity tests (early and late periods) among each season (yellow 0.2-0.3 and red > 0.3)...... 109

LIST OF FIGURES

Figure 1.1: A map of Antarctica. Figure is obtained from Geology.com ...... 14 Figure 2.1: A comparison of spatial patterns of change in mean surface temperature from 1957-2006...... 21 Figure 2.2: Comparison of seasonal pattern of changes from 1957–2006 for O‟Donnell et al. (2011) reconstructions and Steig et al. (2009) reconstruction ...... 22 Figure 2.3: Mean 700 mb geopotential height in January, with 50m contour interval ..... 27 Figure 2.4: Positive SAM condition in the Southern Hemisphere...... 29 Figure 2.5: La Niña condition that shows the sea surface temperature anomaly and the direction of subtropical and polar jets...... 31 Figure 2.6: October averages of total column ozone derived from various satellite sensors - GOME 1 and 2, and SCIAMACHY...... 34 Figure 2.7: Global average of the major and long-lived greenhouse gases since the beginning of 1979, depicting both trends and seasonal cycles ...... 39 Figure 2.8: A schematic representation of the impact of ozone depletion (a) and ozone recovery (b) on the tropospheric circulation...... 40 Figure 3.1: Map of the pressure observation stations used in the point-based reconstructions...... 46 Figure 3.2: Procedure of the Principal Component Regression (PCR)...... 51 Figure 3.3: A diagram of the leave-one-out cross validation method...... 56 Figure 3.4: Procedure of the 30-year calibration and 25-year validation period method. 57 Figure 4.1: Histogram plot of the calibration correlations in each season from the p<0.05 and p<0.10 predictor networks ...... 61 Figure 4.2: Seasonal maps of the calibration correlation magnitude for each Antarctic station that was reconstructed, with the best reconstructions chosen from the two predictor networks...... 63 Figure 4.3: Histogram plot of the validation correlations in each season from the p<0.5 and p<0.10 predictor networks ...... 66 Figure 4.4: Seasonal maps of the validation correlation magnitude for each Antarctic station that was reconstructed, with the best reconstructions chosen from the two predictor networks...... 68 11

Figure 4.5: Histogram plot of the reduction of error (RE) in each season from the p<0.5 and p<0.10 predictor networks ...... 71 Figure 4.6: Seasonal maps of the reduction of error (RE) magnitude for each Antarctic station that was reconstructed, with the best reconstructions chosen from the two predictor networks...... 73 Figure 4.7: Histogram plot of the coefficient of efficiency (CE) in each season from the p<0.5 and p<0.10 predictor networks ...... 76 Figure 4.8: Seasonal maps of the coefficient of efficiency (CE) magnitude for each Antarctic station that was reconstructed, with the best reconstructions chosen from the two predictor networks...... 78 Figure 4.9: Observed trends (black) and the best reconstruction trends (red) in 1957-2011 (DJF 1957-2010) for a) summer, b) autumn, c) winter, and d) spring...... 82 Figure 4.10: Reconstructions at Faraday station during 1957-2011, which represents the best full period station pressure reconstruction and observed record among all seasons. 85 Figure 4.11: Seasonal maps of beta weight magnitude for each predictor station at Faraday from the p<0.10 predictor network...... 89 Figure 4.12: Reconstructions at Bellingshausen station during 1957-2011, which represents the best full period station pressure reconstruction and observed record among all seasons...... 91 Figure 4.13: Seasonal maps of beta weight magnitude for each predictor station at Bellingshausen from the p<0.10 predictor network...... 94 Figure 4.14: Reconstructions at Byrd station during 1957-2011, which represents the best full period station pressure reconstruction and observed record among all seasons...... 97 Figure 4.15: Reconstructions at Syowa station during 1957-2011, which represents the best full period station pressure reconstruction and observed record among all seasons...... 101 Figure 5.1: Decadal comparison of Esperanza (a; West Antarctica) and Mawson (b; East Antarctica) in DJF as the examples used to show most of the distinctive features...... 115 Figure 5.2: Decadal comparison of Faraday (a; West Antarctica) and Dumont d‟Urville (b; East Antarctica) in MAM as the examples used to show most of the distinctive features...... 116 Figure 5.3: Decadal comparison of Bellingshausen (a; West Antarctica) and Casey (b; East Antarctica) in MAM as the examples used to show most of the distinctive features...... 118 12

Figure 5.4: Decadal comparison of Bellingshausen (a; West Antarctica) and Mawson (b; East Antarctica) in SON as the examples used to show most of the distinctive features...... 119 Figure 5.5: 7-yr (blue) and 11-yr (red) smoothed data of Novolazarevskaya in austral summer (DJF) as an example of most of the distinctive features...... 121 Figure 5.6: 7-yr (blue) and 11-yr (red) smoothed data of Dumont d‟Urville in austral autumn (MAM) as an example of most of the distinctive features...... 123 Figure 5.7: 7-yr (blue) and 11-yr (red) smoothed data of Davis in austral winter (JJA) as an example of most of the distinctive features...... 124 Figure 5.8: 7-yr (blue) and 11-yr (red) smoothed data of Casey in austral winter (SON) as an example of most of the distinctive features...... 125 Figure 5.9: Reconstruction trends in 1905-1956 (black) and 1957-2011 (DJF 1957-2010; red) comparison for a) summer, b) autumn, c) winter, and d) spring...... 129 Figure 5.10: 30- (blue), 40- (red), 50- (green), and observed 30-year (purple) running trends at Faraday (a; West Antarctica) and Mawson (b; East Antarctica) in DJF as the examples used to show most of the distinctive features...... 132 Figure 5.11: 30- (blue), 40 - (red), 50- (green), and observed 30- year (purple) running trends at Esperanza (a; West Antarctica) and Mirny (b; East Antarctica) in MAM as the examples used to show most of the distinctive features...... 133 Figure 5.12: 30- (blue), 40- (red), 50- (green), and observed 30-year (purple) running trends at Esperanza (a; West Antarctica) and Davis (b; East Antarctica) in JJA as the examples used to show the distinctive features...... 135 Figure 5.13: 30- (blue), 40- (red), 50- (green), and observed 30- (purple) year running trends at Esperanza (a; West Antarctica) and Davis (b; East Antarctica) in JJA as the examples used to show the distinctive features...... 136

CHAPTER 1: INTRODUCTION

Climate change is a long-term alteration in global weather patterns. In the last few decades, society‟s attention has been captured by topics such as global warming and reducing sea ice extent in the Polar Regions. Nevertheless, weather and climate in a place like Antarctica is still not understood as much as in other regions on the Earth due to its remote location, harsh weather conditions, and a relatively short instrumental record.

Antarctica is the southernmost continent and contains a majority of the fresh water (in the form of ice) on the planet. Changing the climate in ways that result in melting or freezing of the sea ice can highly alter the overall sea level and global ocean circulations worldwide. Hence, it is crucial to understand long-term climate variability in Antarctica.

Antarctica is the coldest place in the world on average. It is within the Antarctic

Circle (parallel to the equator at 66° 33‟ 44‟‟ S) except for part of the Peninsula, at about

66°33‟, and is surrounded by the Southern Ocean. The rock and permanent ice of

Antarctica covers about 14.2 million km2, making it the fifth largest continent after Asia,

Africa, North America, and South America (McGonigal and Woodworth 2001). About

98% of Antarctica is covered by ice with an average thickness of 1.61 km. It is also the largest ice sheet and largest fresh water reservoir in the world. Physically, Antarctica can be divided into Western and Eastern Antarctica by the Transantarctic Mountains, which extend from the Weddell Sea toward the Ross Sea (Fig. 1.1).

East Antarctica consists of high elevation ice sheet with an average height of 3000 m, while West Antarctica has an average elevation of only about 1000 m with vast ice shelves. The weather in Antarctica makes it one of the most extreme locations in the 14 world. On average, it is the coldest, driest, and windiest continent, with an average annual precipitation lower than 20.32 cm (McGonigal and Woodworth 2001). The Arctic and

Antarctic are both Polar Regions, yet Antarctica is vastly different from the Arctic. While

Antarctica is a continent surrounded by oceans, with high elevation, thick frozen ice sheets, and annual mean temperatures of -58 °F at the South Pole, the Arctic is an ocean surrounded by continents, has limited land ice, and has a considerably higher mean annual temperature of 0 °F at the North Pole.

Figure 1.1: A map of Antarctica. Figure is obtained from Geology.com

The first decade of the 21st century was the globally-averaged warmest decade since records started in 1850 (WMO 2012). During this period, nine out of ten years had record warmth, with 2010 as the warmest year. Since 1900, research have shown that the global average temperature has increased by approximately 0.8 ± 0.1°C (Scafetta and

West 2008; Karl and Church 2011). However, in the last few decades, other evidence shows that the Antarctic climate has been changing dramatically (Thompson and

Solomon 2002; Steig et al. 2009). In West Antarctica and the Antarctic Peninsula, most of the regions have been experiencing a warming trend, and have undergone the most rapid warming on the planet during the last fifty years (Gillett et al. 2008; O‟Donnell et al. 2011), warming faster (at the rate of five times) than the global average rate (Fogt et al. 2010).

Surface pressure is a key variable in explaining climate because it is a manifestation of the upper-level atmospheric circulation (Simmonds et al. 2003; Compo et al. 2011). Using it, pressure systems such as cyclones (i.e., low pressure centers) can be identified, which act to re-establish equilibrium in the atmosphere by transferring, among other properties, relatively warm maritime air and cold continental air to different locations, thereby impacting the climate of coastal regions. Across the Southern

Hemisphere, the Southern Annular Mode (hereafter SAM) can explain the majority of surface pressure variability. This pattern is the leading mode of climate variability from daily to yearly time scales (Kidson 1999; Baldwin 2001; Thompson and Woodworth

2011), and is characterized by the strength of the pressure gradient between middle and high latitudes along with the ensuing changes in the strength of the zonal (east-west) flow 16 in the Southern Hemisphere extratropical regions. Notably, the SAM has shown a positive trend over the last fifty years (Jones et al. 2009). In the positive phase, warmer than average conditions along the Antarctic Peninsula and below average temperature conditions across coastal East Antarctica and the interior of the continent (Marshall 2007) are often observed. Interestingly, the recently released „State of the Climate in 2011‟ indicates a cooling trend across the northern and eastern Antarctic Peninsula during a positive SAM period, even though it is not statistically significant as authors only use

2000-2011 records to calculate the trends. Moreover, the warming trends still exist when the full station record until year 2000 is used (McGrath and Steffen 2012).

Due to the aforementioned impacts on temperature, as well as similar impacts on precipitation, sea ice, and the surface current within the Southern Ocean (Zwally et al.

2002; Lefebvre et al. 2004; Gillett et al. 2006; Karpechko et al. 2009), an important component in understanding Antarctic climate change is the interpretation of the variations in the atmospheric circulation. These circulation changes often occur at the mid- and upper-level geopotential height patterns in the atmosphere. Limited resources have made it even harder to understand the historical circulation changes, or even accurately produce reconstructions of them across Antarctica. However, in the last few years, several resources have become available that can potentially be used for interpreting the Antarctic surface pressure as far back as 1905 or earlier. The first one is the 20tb Century Reanalysis (20CR) project, which is a gridded climatological data set from 1871 to 2010 based only on sea surface temperature, surface pressure, and sea ice condition (Compo et al. 2011). The Hadley Centre Sea Level Pressure dataset (HadSLP2) 17 is another useful surface pressure dataset on a 5-degree latitude-longitude grid spacing from 1850-2004, using only pressure observations from 2,228 stations worldwide (Allan and Ansell 2006). Although these are great resources for examining the historical

Antarctic surface pressure variations, it is important to not blindly rely on these data as they are not purely observations, and errors exist. Additionally, the SAM index

(Marshall 2003; Jones et al. 2009; Fogt el al. 2009) reconstructions also provide a way to validate the circulation variations across the Southern Hemisphere in the 20th century, although this index only depicts hemisphere-wide changes in the atmospheric circulation and may not necessarily represent local circulation changes across specific regions of

Antarctica.

Despite the small number of data sources, a few studies have attempted to reconstruct Antarctic circulation variability back to at least 1905 (or even earlier). Yet, they are based on a small ice core network (Russell and McGregor 2010) or subantarctic tree-ring records (Villalba et al. 1997; Jones and Widmann 2003), which all involve comparably noisy (i.e., small signal-to-noise ratios) predictor data. Moreover, given that staffed weather observation stations are sparsely scattered across Antarctica, their records are mainly short term (most began in 1957), and the known deficiencies in reanalysis interpretation of conditions over the continent prior to 1979, as the satellite sounding era began in 1979 that significantly improves the quality of the data and covered areas

(Bromwich and Fogt 2004; Bromwich et al. 2007), there is ample opportunity for improvement. 18

The primary goal of this thesis is to make a first step to improve upon these methods and generate a new Antarctic pressure dataset as far back as 1905, using a

Principal Component Regression (PCR) reconstruction method similar to that which

Jones et al. (2009) and Jones and Widmann (2003; 2004) are used. By only using direct weather station pressure observations across the Southern Hemisphere that are statistically related to the individual pressure records in Antarctica, a more reliable dataset than currently exists can be obtained. In addition, this study will also investigate how these reconstructions depict the uniqueness of ongoing climate change in Antarctica through investigating changes in the atmospheric circulation over a much longer period.

Two main questions will be analyzed based on an Antarctic station-based (point) surface or sea level pressure reconstruction:

1) How feasible is a station-based Antarctic pressure reconstruction going back to

1905? In other words, what level of accuracy is attainable for a station-based

Antarctic pressure reconstruction?

2) What do these reconstructions tell us about the uniqueness of ongoing Antarctic

climate change? In other words, how do the reconstructions represent or depict

Antarctic climate change during 1905-2011?

The first question focuses on the creation of a station-based pressure recontruction that is the longest (ideally to 1905) and the most reliable. This procedure is then tested by performing two independent validation approaches, which are discussed in the Capter 3 methodology section. 19

For the second question, the reconstruction data are analyzed and the uniqueness of the Antarctic climate change is assessed. The highest deviations (peaks and troughs) are identified and compared to ongoing climate events; these features may possibly portray significant events before 1957, when the instrument records were not yet started.

This thesis is constructed as follows. Chapter 2 provides a detailed description of the relevant literature, which includes the background information on the ongoing climate change in Antarctica, the variability of the main climate modes in the Southern

Hemisphere, and an overview of the anthropogenic forcing. Chapter 3 presents a description of the data employed in the study, the statistical reconstruction methods, and the validation methods used to examine the pressure reconstructions. Chapter 4 examines the accuracy of the Principal Component Regression (PCR) models in producing reliable pressure reconstructions, in which these new records will be compared with the observed record. With the longer historical record, in Chapter 5 the uniqueness of the ongoing

Antarctic circulation changes are examined and analyzed using the reconstructions and observation records, with any unique circulation changes in the last 100+ years then being determined. Finally, a summary and conclusions, including the remarks discussed in this thesis and the direction of future work, are presented in Chapter 6.

CHAPTER 2: LITERATURE REVIEW

2.1 Temperature trends in Antarctica

Globally averaged surface temperature has been increasing rapidly, becoming 0.8

± 0.1°C warmer since 1900 (Scafetta and West 2008; Karl and Church 2011). The first decade of the 21st century was the warmest decade since records started in 1850, with the overall globally averaged surface temperature estimated 0.46 °C higher than the long- term average from 1961-1990 (WMO 2012). In years 2000-2010, nine of the ten were the warmest on record. Particularly 2010 was the warmest year, with a mean temperature

0.53 °C above the long-term average, closely followed by 2005 (WMO 2012).

Antarctic surface temperature has also been warming, but not uniformly, as indicated from Antarctic weather station data beginning in the 1950s. The warming is particularly strong in the Antarctic Peninsula, where the strongest annual warming trends are found on its western and northern sides (McGrath and Steffen 2012), with a statistically significant trend (p<0.05) at 0.56 °C decade-1 over 1950-2000 (Turner et al.

2005). In terms of seasonality trends, studies have shown the greatest warming trends along the western Peninsula and in West Antarctica during the winter, while the greatest warming trends existed along the eastern Peninsula during the summer (Turner et al.

2005; Steig et al. 2009; O‟Donnell et al. 2011); yet Steig et al. (2009) and O‟Donnell

(2011) yield slightly different results. O‟Donnell et al. (2011) tested and compared Steig et al.‟s (2009) results with two improved reconstruction methods, which produced overall similar outcomes but particularly disagreed with the latter result, noting improper calibration of satellite data; improper determination of spatial structure during infilling, 21 which distorts the spatial distribution and magnitudes of temperature trends; and suboptimal determination of regularization parameters that is related to satellite principal component retention. Steig et al. (2009) used two methods to overcome these problems.

The first method uses temporal relationships between the satellite and ground data; the second method combines the spatial component of the satellite data with ground data.

They showed that the significant warming regions extended toward the Ross Sea over

1957-2006, while the O‟Donnell et al. (2011) analysis constrained the significant warming in the area adjacent to the Peninsula, as shown in Fig. 2.1.

Figure 2.1: A comparison of spatial patterns of change in mean surface temperature from 1957-2006. O‟Donnell et al. (2011) reconstructions (left and center) and Steig et al. (2009) reconstruction (right). Figure is reprinted with permission and collected from O‟Donnell et al. (2011).

In terms of seasonal patterns of temperature change (Fig. 2.2), both studies agree the Antarctic Peninsula experiences the strongest warming through the seasons, particularly in austral winter (June – August), with the temperature increasing as much as

0.6°C in the O‟Donnell et al. reconstructions (2011). Warming with temperature increases as much as 0.3°C can also be seen in West Antarctica during the austral spring 22

(September – November; Steig et al. 2009; O‟Donnell et al. 2011). On the other hand, overall temperature at the interior region and throughout East Antarctica remained stable or cooled slightly across the seasons over 1957-2006.

Figure 2.2: Comparison of seasonal pattern of changes from 1957–2006 for O‟Donnell et al. (2011) reconstructions (left and center) and Steig et al. (2009) reconstruction (right). Figure is reprinted with permission and collected from O‟Donnell et al. (2011).

Turner et al. (2005) investigated the surface temperature over a period of 50 years since the 1950s from 19 stations. They also noted the asymmetrical trends and found that seasonal warming trends at the Antarctic Peninsula and West Antarctica, such as at the

Faraday station on the western side of the Antarctic Peninsula, had increased at a rate of

0.56 °C per decade; whereas a slight cooling trend was present in East Antarctica. Thus,

Turner et al. (2005) agree with the O‟Donnell et al. (2011) findings that a warming trend existed in the Antarctic Peninsula and West Antarctica, while a slight cooling trend existed in East Antarctica.

In contrast to the strong warming trends observed during the second half of the

20th century, 5 out of 7 Antarctic Peninsula stations have shown a slight annual cooling temperature trend between 2000 and 2011, as shown in Table 2.1. The cooling has been strongest at the northern Antarctic Peninsula, specifically in the Bellingshausen,

O‟Higgins, and Esperanza stations, with a cooling trend of -0.7°C /decade in the last 15 years (McGrath and Steffen 2012). Nevertheless, the time period is too short to claim as statistically significant. Moreover, general warming trends (except in the Esperanza and

Larsen C stations), with p<0.05 confidence intervals, were seen at Antarctic Peninsula stations from the 1950s to 2000 (Table 2.1; McGrath and Steffen 2012).

Table 2.1: Antarctic Peninsula station annual mean air temperatures and trends with 95% confidence interval. Table is reprinted with permission and obtained from McGrath and Steffen (2012).

Sea ice changes can also reflect temperature changes in Antarctica as they are in part thermodynamically connected: warmer conditions are often associated with less sea ice and vice-versa. Dynamically, changes in the atmospheric circulation can also impact sea ice extent/concentration around Antarctica through Ekman transport. When the circumpolar flow around Antarctica is characterized with particularly strong westerlies, the net transport of water at depth is 90o to the left, or northward. At the surface, a northward component of the ocean circulation advects ice farther equatorward, leading to a greater sea ice extent during periods of strong westerly circumpolar flow. The equatorward subsurface flow can further induce a cooling of sea surface temperatures along the coastal regions as cold deep ocean water upwells, which can also increase the ice cover (Stammerjohn et al. 2008). Moreover, the sea ice condition can also be altered by the location and intensity of the pressure systems through their associated winds, 25 which are related to changing the surface temperature. For instance, the semi-permanent low pressure system named Amundsen-Bellingshausen Seas Low (hereafter ABSL) is often situated near the Amundsen-Bellingshausen Seas (Fogt et al. 2012). This low pressure system rotates clockwise in the Southern Hemisphere, and therefore would transfer warm maritime water from the east side of the system, and cold continental air on its western side (Stammerjohn et al. 2008).

Sea ice extent or retreats that are mainly caused by changing surface temperature can also be seen by using satellite imagery. The Stammerjohn et al. (2008) study shows a decrease in the concentration and duration of sea ice from the western regions of the

Antarctic Peninsula extending to the Amundsen-Bellingshausen Sea regions.

Stammerjohn et al. (2012) also found that during the period of 1979-2010, Antarctic sea ice retreated one month earlier and advanced two months later in the Antarctic Peninsula and Bellingshausen Sea during the summer, whereas the sea ice retreated one month later and advanced one month earlier in the western Ross Sea region, indicating an asymmetrical change in Antarctic sea ice extent. These results confirmed Liu et al.

(2004), who found that the concentration of sea ice in the Amundsen-Bellingshausen

Sea/western Weddell areas were below average, and the length of the ice season had decreased over the period 1979-2002, with a decreasing rate of 4-10% per decade at the

Bellingshausen Sea.

It is intuitive to expect that the total amount of Antarctic sea ice would decrease as the Antarctic surface experiences a warming trend. Surprisingly, the situation is opposite with a weak positive trend of total sea ice in Antarctica (Liu et al. 2004; 26

Cavalieri and Parkinson 2008; Comiso and Nishio 2008; de Mangalhaes et al. 2012). Shu et al. (2012) illustrates the increasing of the total Antarctic sea ice trend from 1979-2009, where the total Antarctic sea ice extent trend has increased slightly across the continent, with 1.36 ± 0.43% per decade from 1979-2006. In fact, the Centre for Australian Weather and Climate Research reported that the Antarctic sea ice extent reached a maximum in record on September 22nd 2012, with 19.453 millions square kilometers (CAWCR 2012).

2.2 Atmospheric circulation in Antarctica

Westerlies are dominant in the mid and upper atmosphere as the winds seek to establish equilibrium with the Coriolis force and pressure gradient force. In the Northern

Hemisphere, typically, the middle to upper (at and above 500 hPa) atmospheric circulation has a more meridional (north-south) component than in the Southern

Hemisphere, primarily because more north-south extending mountain ranges exist in the

Northern Hemisphere. When the atmosphere flows around these high mountain ranges, such as the Rockies, Alps, and Himalayas, standing waves are formed as these mountain ranges act as a barrier that directs the circulation either north or south (meridional flow;

Peixoto and Oort 1992; Reid 2000). The mean wind patterns in the Southern Hemisphere, on the other hand, are more zonally oriented as it is mainly covered with ocean and has comparably fewer mountain ranges. In Antarctica, nevertheless, the higher elevations of

East Antarctica and the Antarctic Peninsula act similarly as mountain ranges, forcing the circulation to have a meridional component, ultimately resulting in a common flow pattern named zonal wave three (Baines and Fraedrich 1989; Raphael 2004). This concept/terminology refers to the number of troughs (low pressure system) and ridges 27

(high pressure system) present at a certain latitude. The presence of these semi-permanent cyclones and anticyclones highly alters the Antarctic climate as they transfer and redistribute heat and energy via the atmospheric circulation (Stammerjohn et al. 2008;

Fogt et al. 2012). Baines and Fraedrich (1989) show that zonal wave three is a dominant wave pattern at around 60°S that can be observed through the troposphere and is substantially barotropic, implying that the temperature gradient is parallel to the pressure gradient (i.e., the atmosphere is not characterized by temperature advection). Fig. 2.3 shows the location of three semi-permanent low pressure systems situated at the Weddell

Sea, Amundsen-Bellingshausen Seas, and Mackenzie Sea, which comprise the zonal wave three pattern in the high latitudes of the Southern Hemisphere. On climatological timescales, these three semi-permanent lows alter the direction and strength of the wind, corresponding to temperature and sea ice variations as previously mentioned.

Figure 2.3: Mean 700 mb geopotential height in January, with 50m contour interval. Figure is reprinted with permission and collected from Hofmeyr (1957). 28

To better understand the reason behind the non-uniform change in Antarctic surface temperature and sea ice extent/concentrations, it is necessary to know the behavior of the Antarctic atmospheric circulation. Changes in the atmospheric circulation play an important role in explaining the weather features, as variations of high and low pressure can alter the strength and direction of the wind, surface temperature, and sea ice conditions. One such cylone is the ABSL (Fogt et al. 2012).

The ABSL is a climatological region of low pressure roughly located in between

45°-75°S, 180° - 60°W that often experiences strong cyclones. It is situated in the vicinity of the Amundsen and Bellingshausen Seas. Recently, the significant sea ice loss in the

Amundsen-Bellingshausen Seas described previously was linked to the ABSL circulation, with warm maritime air advection toward the Antarctic Peninsula and cold continental air advection toward the eastern Ross Sea (Lefebvre et al. 2004; Stammerjohn et al. 2008). Fogt et al. (2012) studied the synoptic activity of the ABSL, where they identified that more than 550 low pressure systems existed in a year at the ABSL region.

They also discovered that austral spring (September – November) is the season that has the most intense cyclonic activity in the Southern Hemisphere.

2.2.1 Southern Annular Mode (SAM)

In the Southern Hemisphere, the two most relevant climate modes that can explain the greatest climate variability are SAM and the El Niño – Southern Oscillation

(ENSO). SAM, also referred as the Antarctic Oscillation (AAO), is the dominant pattern, with an annular structure over Antarctica that can explain atmospheric circulation variability operating from day-to-day to year-to-year time scales (Kidston 1999; Baldwin 29

2001; Thompson and Woodworth 2011). It is characterized by pressure anomalies of one sign across Antarctica and anomalies of opposite sign at about 40-50° S. During the positive phase, the Antarctic pressure is below average while the pressure at mid-latitude is higher than average, producing a stronger polar jet (Fig. 2.4); in the negative phase, the condition is opposite to the positive phase, leading to a weaker polar jet as the pressure gradient between mid-latitude and high latitude is weaker (Thompson and Woodworth

2011). Fogt et al. (2009) analyzed trends and variations in three seasonal SAM reconstructions, and noted that each captured a positive SAM trend in austral summer and autumn in the last 50 years, which is the strongest trend in the last 150 years.

Figure 2.4: Positive SAM condition in the Southern Hemisphere. During positive SAM years, warm, maritime air advection occurs in the western Antarctic Peninsula (red arrow), while cold, continental air advection is delivered toward the Ross Sea (white arrow). Figure is reprinted with permission and collected from Stammerjohn (2008). 30

The SAM highly impacts the surface temperature and ice-covered area in the

Southern Ocean. In particular, the ABSL is stronger during the positive SAM years, which situates the Amundsen and Bellingshausen Seas in a more northerly flow, while the Ross Sea tends to have a more southerly flow; these circulation patterns induce surface cooling and ice formation in the Ross Sea and a surface warming and decrease of surface ice in the Amundsen and Bellingshausen Seas (Thompson and Solomon 2002;

Lefebvre et al. 2004).

2.2.2 El Niño – Southern Oscillation (ENSO)

ENSO is another important climate mode that explains the second largest percentage of interannual climate variability in the Southern Hemisphere. It represents a physical relationship between the atmosphere and ocean surface temperature in the equatorial Pacific Ocean. The oceanic portion of ENSO is characterized by three phases, determined by the Pacific sea surface temperature: El Niño (warmer than average sea surface temperatures in the eastern/central Pacific), La Niña (colder than average sea surface temperatures in the same region; Fig. 2.5), and a neutral stage (average sea surface temperature). The neutral stage is associated with strong convection in the western Pacific. During El Niño, the gradient of the thermocline is depressed in the eastern Pacific, as the trade winds are weaker. This reduces the efficiency of upwelling along the coast of Southern America. The warm western Pacific water can now extend eastward; whereas La Niña denotes opposite conditions as the sea surface temperature will be lower than normal across the equatorial Eastern/Central Pacific (Neelin et al.

1998; McPhaden 2003). 31

Chances in the ocean surface temperature during ENSO can also induce changes in the atmospheric pressure, which is known separately as the Southern Oscillation. This term describes the atmospheric pressure differences in the East and West Pacific. During an El Niño event, low surface pressure anomalies situate along the west coast of South

American and central equatorial Pacific, while high pressure anomalies can be found in

East Australia. As before, a La Niña event is characterized by opposite conditions, namely high pressure anomalies in the South American west coast/central equatorial

Pacific and low pressure anomalies near East Australia (Neelin et al. 1998).

Figure 2.5: La Niña condition that shows the sea surface temperature anomaly and the direction of subtropical and polar jets. The ABSL strengthens (“L” black circle) during La Niña, which in turn leads to warmer than average conditions in the Antarctic Peninsula and cooling in the Amundsen-Bellingshausen Seas. Figure is reprinted with permission and collected from Stammerjohn (2008).

Even though ENSO is mostly used to describe climate variability in the Pacific, the Antarctic climate is still influenced via atmospheric teleconnections (Stammerjohn et al. 2008), including those from ENSO. Fogt and Bromwich (2006) show that ENSO has an influence on the ABSL pressure, where the ABSL is weaker during an El Niño event and deeper in a La Niña event. They also demonstrated that the ENSO influence on the

South Pacific is dependent on the phase of the SAM. Stammerjohn et al. (2008) also found a relationship among SAM, ENSO, and the length of the sea ice season at the

Antarctic Peninsula. In general, the ice-atmosphere had the strongest connection when negative SAM was concurring with El Niño and positive SAM was concurring with La

Niña. In the years between 1991-2000, seven out of ten positive SAMs were coincident with La Niña events, which deepened the sea level pressure (ABSL) near the Antarctic

Peninsula, resulting in a stronger northerly flow in the western Antarctic Peninsula and southern Bellingshausen Sea that was associated with a shorter sea ice season.

2.3 Anthropogenic forcing influencing Global Climate

2.3.1 Ozone

Ozone is a greenhouse gas that is highly concentrated in the stratosphere between

12 and 50 km, often referred to as the ozone layer (Reid 2000). It is an essential gas as it absorbs and reflects shortwave radiation like UV-B and UV-C that is harmful to living organisms. Ozone is a form of oxygen that, in its natural state, is formed and destroyed equally in the atmosphere. To describe these natural processes of formation and loss, a chemical reaction can be expressed as in Eq. 2.1:

(2.1) 33

There are three forms of oxygen that are involved in the ozone-oxygen cycle: the oxygen atom (O), oxygen molecule (O2), and ozone (O3). First, an O2 absorbs the UV radiation whose wavelengths are between 2.4-3.2 nm and breaks apart (photolysis) into two oxygen atoms (Reid 2000). The single atomic oxygen atom then combines with another

O2 to create O3. When these ozone molecules absorb the UV light from the sun, the radiation can split the O3 back into its original form that contains an oxygen molecule and an oxygen atom (Eq. 1). This process is called the ozone-oxygen cycle (Reid 2000).

Unfortunately, human activities also play an important role in destroying the ozone layer. Prior to the late 1970s, there were typically 450 DU (Dobson unit) of ozone over Antarctica (Reid 2000). Since 1995, however, nearly every year has seen a severe loss of ozone in Antarctica (Newman et al. 2011; Fig. 2.6). Figure 2.6 shows clearly that smaller ozone holes (deficits in ozone concentrations) have existed, ranging as low as

100-250 DU over Antarctica from 1995-2010, as shown in white, purple, and blue color. 34

Figure 2.6: October averages of total column ozone derived from various satellite sensors - GOME 1 and 2, and SCIAMACHY. The white, blue, and purple denote a low ozone concentration area, indicative of the ozone hole. Ozone concentrations have been consistently low over Antarctica during 1995-2010. Figure is reprinted with permission and collected from Newman et al. (2011).

Human activity has dramatically increased ozone depletion by releasing two substances into the atmosphere which chemically break down ozone - chlorine and bromine (Solomon 1999; Staehelin et al. 2001). Most of the atmospheric chlorine comes from chlorofluorocarbons (CFCs). These were originally used as refrigerants, aerosol propellants, foam blowing agents, and solvents for the electronic industry. Due to the fact that CFCs are a long lasting gas that does not react with other substances through insolation in the troposphere, the CFCs can survive long enough to rise high up into the stratosphere (Reid 2000). Most of the chlorine released from the CFCs consist of 35 important substances like chlorine nitrate (CIONO2) and hydrogen chloride (HCI; Reid

2000). Under normal circumstances, these are harmless substances that are sealed in the stratosphere. However, the CFCs are well suited for chemical reactions when polar stratospheric clouds (PSCs) are present.

During the austral winter, the high speed polar jet stream efficiently traps the cold air inside above Antarctic troposphere, which allows the atmosphere to cool down further below -80ºC and favors the PSCs to form (Reid 2000). There are two ways to form the

PSCs; the first type of PSCs is formed with nitric acid and water when the temperature of the stratosphere reaches below -80ºC; while the second type is formed from ordinary water ice, although it is less common as the stratosphere is much drier with less water vapor existing (Reid 2000). The PSCs allow the chlorine reservoir (CIONO2 and HCI) to interact in the stratosphere and convert into molecular chlorine (Cl2) and nitric acid

(HNO3), as shown in Eq. 2.2:

(2.2)

Cl2 is an active substance that can easily split apart when the radiative energy from the sun is strong enough. Thus, the next chemical reaction takes place (Eq. 2.3) when the sun rises again during the austral spring in Antarctica. The incoming solar radiation breaks the Cl2 into two chlorine atoms, allowing them to freely react with the O3. Each of these chlorine atoms can now react with the O3 and break it down into O2 and chlorine monoxide (CIO; Eq. 2.4; Solomon 1999; Reid 2000). 36

(2.3)

(2.4)

(2.5)

Two O3 with a CIO can further break apart two O3 into three O2 (Eq. 2.5). This cycle continually destroy tens of thousands of ozone molecules before the CIO converts into another substance. Hence, chlorine is a powerful substance that can significantly destroy the ozone layer in the stratosphere.

Bromine is another substance that also plays an important role in ozone destruction (Yung et al. 1980; Lary 1996). It is more devastating than chlorine as it does not require sunlight and is always in a reactive form (Reid 2000). In the atmosphere, the bromine itself first reacts with the oxygen atom to form Bromine oxide (BrO). This substance combines with the CIO, converting it into Br, Cl, and O2 (Eq. 2.6). The Br can now react with O3 and convert into BrO and O2 (Eq. 2.7); from here the Cl continues the process as shown previous in Eq. 2.4.

(2.6)

(2.7)

Hence, not only bromine itself can destroy the ozone, but it also increases the chlorine substances in the stratosphere that promote further ozone destruction (Tung et al. 1986;

McElroy et al. 1986). Fortunately, the total amount of bromine is 100 times less than 37 chlorine, although it is still sufficient enough to destroy the ozone (Wamsley et al 1998;

Reid 2000). Thus, the total column of ozone approaches minimum value in each austral spring, which is the season when the sun starts to rise again in the South Pole (Solomon

1999).

2.3.2 Greenhouse gases

Many chemicals found in the Earth‟s atmosphere act as greenhouse gases. These gases do not effectively absorb shortwave radiation as they pass through the atmosphere.

A portion of this shortwave radiation reaches the surface, where it is primarily absorbed.

The Earth, having a much cooler temperature than the sun, releases longwave radiation into the atmosphere. The greenhouse gases effectively absorb the majority of wavelengths of infrared radiation and trap the heat in the atmosphere, returning radiation towards Earth‟s surface, keeping Earth‟s temperature well above the radiative equilibrium temperature. Thus, greenhouse gases are essential for life to form.

Many greenhouse gases occur in nature such as water vapor, carbon dioxide, methane, and nitrous oxide, while others come from man-made chemicals. Those gases include the chlorofluorocarbons (CFCs), hydrofluorocarbons (HFCs), perfluorocarbons

(PFCs), and sulfur hexafluoride (SF6). At the global scale, nevertheless, the majority of the long-lived greenhouse gases such as carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) are largely present due to human activities. Figure 2.7 shows the global average of these gases from 1978-2012 (Butler 2012). Overall, the concentration among those gases has been increasing rapidly since 1978; CO2 has increased the most dramatically from about 335ppm in 1978 to almost reaching to 400ppm (Fig. 2.7a). 38

Carbon dioxide is primarily contributed from deforestation and using fossil fuels such as using coal, oil, and natural gas (NOAA 2012); this is shown by the general incline of Fig.

2.7a. Furthermore, it is important to notice that the oscillation trend in Fig. 2.7a is due to the fact that a larger portion of CO2 can be converted to oxygen through photosynthesis during the spring and summer in the Northern Hemisphere than in the Southern

Hemisphere, as there is more vegetation in the Northern Hemisphere (seasonal changes).

The second significant greenhouse gas is CH4, or methane. It has risen from

1625ppb in 1983 to as high as 1800ppb in 2012 (Fig. 2.7c). CH4 is exceptionally effective at absorbing radiation, with a lifetime of about 10-12 years in the atmosphere (NOAA

2012). CH4 is produced in natural low oxygen environments, like swamplands, from various biological processes. Nevertheless, the large portions of the recent methane production come from human activities such as growing rice, raising cattle, and using natural gas (NOAA 2012). The third most important greenhouse gas is N2O, or nitrous oxide (Fig. 2.7b). The concentration of N2O has begun to rise since the Industrial

Revolution as it is produced by microbial processes in soil and water from using fertilizers (NOAA 2012), starting at about 300ppb in 1978 to 325ppb in 2012 (Butler

2012). Finally, those gases in Fig. 2.7d are the by-products of the CFCs (refer to Section

2.2.1.), which also show steady increases since 1998. These increasing greenhouse gases due to human activities act as a cap to trap more infrared energy in the atmosphere, which further induces warming in the troposphere.

Figure 2.7: Global average of the major and long-lived greenhouse gases since the beginning of 1979, depicting both trends and seasonal cycles (specifically CO2 and CH4). Figure is reprinted with permission and adopted from Butler (2012).

2.3.3 Impact of ozone and greenhouse gases

Studies have demonstrated that ozone depletion and increasing greenhouse gases play a significant role in tropospheric and stratospheric circulations (Thompson and

Solomon 2002; Arblaster and Meehl 2006; Cai and Cowan 2007; Fogt et al. 2009; Son et al. 2010). Lorenzo et al. (2011) have shown that ozone depletion is the main driver of 20th century atmospheric circulation changes in the Southern Hemisphere. The ozone hole not only shifts the position of the polar tropopause and polar jet stream; it also extends through the entire Southern Hemisphere, resulting in the expansion of the Hadley cell and subtropical dry zone (Lorenzo et al. 2011). Fig. 2.8a demonstrates the behavior of the 40 atmospheric circulation in ozone depletion. It results a decrease of the lower stratospheric temperature as less ozone exists to trap the radiation, followed by an increase of the polar tropopause height, a poleward shift of polar jet, and the Hadley cell expansion into a higher latitude (Son et al. 2010). The opposite is predicted when the ozone recovers, as shown in Fig. 2.8b.

Figure 2.8: A schematic representation of the impact of ozone depletion (a) and ozone recovery (b) on the tropospheric circulation. The arrows show the movement of a particular feature such as the Hadley cell, the subpolar jet and the height of the polar tropopause. Figure is reprinted with permission and adopted from Son et al. (2010).

Temperature in the troposphere has higher concentration of greenhouse gases, particularly in the tropics, as more longwave radiations are able to trap by greenhouse 41 gases in the lower troposphere. The warmer sea surface temperature increases atmospheric convection, which further increases the warming in the middle and upper troposphere through latent heat release. The warmer tropics increase the height of the tropical tropopause and steepen the gradient of the tropopause from the Equator to the

Pole, favoring stronger westerlies (Arblaster and Meehl 2006; Fyfe et al. 2007). Hence, decreasing stratospheric ozone concentrations and increasing greenhouse gases are significantly related to the positive trend in the SAM, especially during austral summer and autumn, as those are the gases that influence the atmospheric circulations (Thompson and Solomon 2002; Arblaster and Meehl 2006; Marshall 2007). Thompson and Solomon

(2005) illustrated that the largest changes of the stratosphere have been found during the spring time. This signal can have a lag of 2 months until it is able to propagate to the troposphere, which confirms the SAM is stronger during the austral summer. Even though extensive research has been done in Antarctica, previous studies employ with relatively short-period data. As such, many uncertainties still remain and more research is required. For this reason, a longer time series of Antarctic surface pressure (1905-2011) will enable researchers to examine the uniqueness of these circulation changes in light of a longer historical reference series.

2.4 Chapter Summary

In light of all the articles that have been mentioned in this chapter, it is important to understand that there are many factors that have to be considered in order to understand the complexity of Antarctic surface pressure variations. In terms of temperature, even though many studies (Steig et al. 2009; O‟Donnell et al. 2011) have 42 shown a warming trend at the Peninsula since 1957, by using the temperature record in

2000-2011, most of the Antarctic Peninsula stations have shown a slightly cooling trend

(McGrath and Steffen 2012). Due to the fact that Antarctic station records are fairly short

(most beginning around 1957), it is difficult to verify the uniqueness of the ongoing

Antarctic surface temperature trend. In terms of climate modes, even though SAM and

ENSO indices are well reconstructed, these synoptic climate patterns might not be able to depict the local climate variability with much accuracy. Moreover, it has been demonstrated that ozone depletion and greenhouse gases are connected to atmospheric circulations. The “ozone killer” – CFCs – did not widely come into use until the 1930s, indicating a larger portion of ozone in the stratosphere would be expected before 1930.

On the other hand, the total amount of greenhouse gases due to human activities was much less in the early 20th century when compared with the present. Thus, the global mean temperature would be expected to be cooler in the earlier time. With all these factors in mind, would the surface pressure in Antarctica be significantly different or about the same in the early 20th century? Currently, the answer to this question is uncertain. Therefore, this research seeks to find reliable reconstruction data that can extend farther back in time before 1957 in order to better explain and understand the ongoing climate changes in Antarctica in light of the many compounding factors outlined in this chapter.

CHAPTER 3: DATA AND METHODS

3.1 Data

The Antarctic station-based surface pressure reconstructions consist of a large set of climatological pressure data across the Southern Hemisphere continents and

Antarctica, primarily focusing on seasonal and annual timescales. Most of the data can be accessed through Dr. Ryan Fogt, as much of this data is archived at Ohio University, and has already been updated through 2011. For this thesis, four datasets will be used in this study.

3.1.1 South Hemisphere Mid-latitude Stations

In order to have the most complete long-term Southern Hemisphere station records extending back as far as 1905 or earlier, three reliable data archives will be used.

These pressure data observations are acquired from the Global Historical Climatology

Network (GHCN; Peterson and Vose 1997; Peterson et al. 1998); dataset ds.570.0; and the Climatic Research Unit (Jones 1988; New et al. 1999).

GHCN is one of the primary references of climatic data used for climatology. It is a large dataset that consists of temperature, pressure, and precipitation records with a total of 7,280 temperature stations, 20,590 precipitation stations, and 2,668 sea level pressure stations worldwide (Peterson and Vose 1997). It is operated by the National

Climatic Data Center. GHCN was first released in the early 1990s and has been continuously updated since. All the historical and real-time data involved undergo quality assurance reviews, including raw data checks, time series checks to identify changes of the mean and variance, spatial comparisons to verify the certainty with the climatological 44 mean and seasonal cycle, and a neighboring stations check to distinguish outliers

(Peterson and Vose 1997). The data can be accessed at http://www.ncdc.noaa.gov/ghcnm

/v2.php.

Dataset ds.570.0 (the World Monthly Surface Station Climatology) is another reliable dataset that is operated by the University Corporation for Atmospheric Research

(UCAR). It has a total of over 4,700 different stations, with 2,600 more added in recent years from all over the world. Some of the stations date to the mid-17th century; the temporal range is from January 1st, 1738 to the present. The majority of the data were directly collected from the National Climatic Data Center (NCDC) of the National

Oceanic and Atmospheric Administration (NOAA), with datasets patched with data from

Harvard College Observatory (HCO), the Meteorology Department (METEO) from

Florida State University, UCAR, the National Center for Atmospheric Research (NCAR), the Global Dynamics Division (CGD), and the Climate Analysis Section (CAS; Quayle

1989). The dataset contains a variety of climate variables such as sea surface/surface air temperature, sea level/surface air pressure, precipitation, humidity, and geopotential height. The ds.570.0 dataset is also quality-controlled by UCAR and can be accessed at http://rda.ucar.edu/datasets/ds570.0/.

The third dataset that is used for the Southern Hemisphere mid-latitude station is from the Climate Research Unit (CRU), which is operated by the University of East

Anglia in the United Kingdom. Much of these data have been obtained directly from Dr.

Philip Jones, and were used in previous Southern Hemisphere reconstructions in Jones et al. (2009) and Fogt et al. (2009). 45

3.1.2 Antarctic stations

The Antarctic observation data is collected from the British Antarctic Survey – specifically, the Reference Antarctic Data for Environmental Research archive

(READER; Turner et al. 2004), operated by the Scientific Committee on Antarctic

Research (SCAR). The primary sources of data are collected from Antarctic research stations and automatic weather stations at fixed locations. The dataset contains various meteorological variables such as surface temperature, mean sea level pressure, surface wind speed, upper air temperature, and geopotential height. Most of the Antarctic stations began recording around 1957 and only those stations with continuous data collection (no substantial multi-year gaps) are used. This dataset is quality-controlled and provides the most complete and reliable Antarctic pressure data (Turner 2004). The data can be accessed at http://www.antarctica.ac.uk/met/READER/.

Overall, there are 18 key Antarctic stations (17 staffed and 1 patched part staffed, part automatic weather station) that will be used as a predictand for the reconstruction over the 1905-2011 period (Table 3.1). Further, 29 mid-latitude predictor stations are depicted with data beginning in1905 or earlier (Fig. 3.1). In terms of the PCR models,

Table 3.2 shows the number of predictors (red circle in Fig. 3.1) that are used in the PCR model for each station, in both the p<0.05 and p<0.10 networks, respectively.

Figure 3.1: Map of the pressure observation stations used in the point-based reconstructions. Red circle = Antarctic stations (predictands); Blue circle = mid-latitude predictor stations whose record starts in at least 1905.

Table 3.1: Antarctic stations used in the point-based reconstructions. Lat = latitude (negative = degree of south); Long = longitude (negative = degree of west, positive = degree of east); % is the percent of completeness of the station record. All stations are staffed except Byrd, which is patched with a staffed station and automatic weather station (AWS).

Station Name Lat Long Start yr % Mirny -66.6 93 1956 99.7

Novolazarevskaya -70.8 11.8 1961 99.84

Halley -75.5 -26.7 1956 99.55 Faraday -65.3 -64.3 1950 99.46 Mawson -67.6 62.9 1954 99.43 Dumont d'Urville -66.7 140 1956 99.4 Casey -66.3 110.6 1960 99.36 Amundsen-Scott -90 0 1957 100 Bellingshausen -62.2 -58.9 1959 100 McMurdo/Scott Base -77.9 166.8 1956 99.55

Vostok -78.5 106.9 1958 96.45

Marambio -64.2 -56.7 1970 96.43

Marsh/O’Higgins -62.2 -58.9 1963 95.92 Esperanza -63.4 -57 1945 94.76 Davis -68.6 78 1957 91.82 Syowa -69 39.6 1957 90.91 Rothera -67.6 -68.1 1946 88.64 Byrd -80 -119.4 1957 76.06

Table 3.2: The total number of predictors in each station (both p<0.05 and p<0.10 networks in each season) that are used in the PCR models.

Number of Predictors in 5% and 10% Networks DJF MAM JJA SON Stations 5% 10% 5% 10% 5% 10% 5% 10% Amundsen-Scott 20 22 12 16 11 14 6 7 Bellingshausen 14 14 3 6 8 8 4 4 Byrd 15 17 10 12 8 12 11 13 Casey 21 21 6 12 13 15 8 12 Davis 19 22 13 15 12 15 6 10 Dumont d'Urville 22 23 13 14 13 14 14 16 Esperanza 12 14 3 5 5 7 2 4 Faraday 13 15 3 4 7 8 3 3 Halley Bay 19 21 6 8 14 14 9 9 Marambio 5 9 4 4 6 7 2 5 Marsh/ O’Higgins 9 10 4 6 8 8 2 4 Mawson 19 21 14 15 8 12 4 8 McMurdo/ Scott Base 18 21 11 14 13 14 10 11 Mirny 16 17 7 13 14 15 6 9 Novolazarevskaya 20 20 11 16 11 12 5 11 Rothera 10 14 3 5 8 8 3 3 Syowa 15 16 9 12 9 10 8 10 Vostok 17 20 14 17 13 14 10 13

3.2 Methodology

To reconstruct the historical pressure record among those 18 Antarctic stations listed above, principal component regression (PCR) is performed. This method was first used by Briffa et al. (1986) in climate reconstructions to estimate the summer sea level pressure in England from tree ring data. Since then, many other researchers have used this technique to develop successful climatological reconstructions, examples of which are the SAM index (Jones et al. 2009; Fogt et al. 2009), drought indices from tree ring 49 data (Cook et al. 1999), the North Atlantic Oscillation index (Cook et al. 2002), and well- known temperature reconstructions (Mann et al. 1999).

3.2.1 Correlation analysis

Correlation analysis is the foremost technique used to show the relationship between the predictors and predictand. For each station in Table 3.1, a set of predictor stations are established by extracting mid -latitude stations (blue dots in Figure 3.1) that are significantly correlated with a specific Antarctic station (predictand). The thesis will test both the p<0.05 and p<0.10 significance levels (i.e., probability of zero correlation) by using a t-test in order to determine the sensitivity of the reconstruction to the predictor network. The t-test is a statistical hypothesis test that follows the t-distribution and tests if the null hypothesis is supported. In this thesis, the null hypothesis is that there is no significant correlation between the predictor and predictand. The equation of the t-test for a given correlation, r, is as follows:

√ (3.1)

where N is the sample size (ideally 1957-2011, depending on the completeness of the data) and r is the correlation coefficient between the predictor and predictand. The degrees of freedom are as follows, as it is assumed that each season is independent of both the following and preceding year:

(3.2)

Correlations using station data are presented as statistical significance instead of correlation magnitude, as the station records have different completeness of years and thus have different degrees of freedom.

3.2.2 Principal Component Regression (PCR)

Once the data from the mid-latitude stations (predictors) are collected, PCR can be employed (Fig. 3.2). The PCR methodology first uses the covariance matrix of the selected mid-latitude stations (predictors) and performs principal component analysis to separate the covariance matrix into distinct (orthogonal) modes of variability. In this step, the principal component coefficients, which relate these modes to the original data, are then obtained. Each of these modes consists of a spatial pattern, named empirical orthogonal functions (EOFs), and a time series that describes the amplitude of each pattern, named PCs. A subset of these PCs is then subsequently regressed, using ordinary least-squares multiple linear regression, onto the predictand to ultimately relate the predictors to it. This step retrieves the regression coefficients, relating the retained PCs to the predictand. The weights for each station (predictors) are then calculated by matrix multiplying (projecting) the PC coefficients and regression coefficients. The weight is the portion that each predictor shares with the retained PCs and the relationship of these PCs with the predictand. The final result of the reconstruction is simply the sum of all the weighted predictors. In conclusion, the goal of performing PCR is to extract the portion of data from the large dataset (predictors) that can explain the most variability of the given predictand. By only retaining a subset of PCs, PCR is superior to multiple linear 51 regression; unwanted noise in the predictor network is essentially filtered out during this process, thus allowing for a better calibration of the predictors to the predictand.

Figure 3.2: Procedure of the Principal Component Regression (PCR).

3.2.3 Trend analysis

The trends are calculated using least-squares linear regression analysis when the reconstruction data are determined. The goodness of fit regression line determines the significance of the trends within the data. The slope of the independent variable x (here, time) and the dependent variable y (here, pressure) is calculated as follows:

∑ (∑ ) ∑ (3.3) ∑ ∑

Then, the standard error of the slope sb is calculated as the main measure of uncertainty of the goodness of fit of the regression line, which is calculated as:

( ∑ *∑ +*∑ +) √ [ ∑ (∑ ) ] ∑ ∑

(3.4)

√∑ ∑

The standard error shows how well the fitted equation fits the sample data. Since the residuals, or the distance between the regression line and the observed y-value, are normally distributed, the next step is to calculate the t -test as:

(3.5)

53 where bo is the hypothesized slope. Each trend is tested with the null hypothesis that the hypothesized slope (bo) is equal to zero. Since the y-values (pressure) are not necessarily independent, the degrees of freedom are reduced by lag-1 autocorrelation and set to be n-

2, in order to have a more conservative estimation of the statistical significance of the trends.

3.3 Validation Methods

Antarctic station pressure reconstructions are accomplished by using 17 staffed stations and 1 automatic weather station (Table 3.1) during the period of 1905-2011. The calibration period typically starts in the International Geophysical Year (1957) when most of the Antarctic station records began. The reconstructions primarily focus on seasonal and annual timescales that have shown a good representation for the SAM reconstructions (Fogt et al. 2009; Visbeck 2009) and Antarctic temperature reconstructions (Steig et al. 2009; O‟Donnell et al. 2011). To address the uncertainty and potential inaccuracy of the result, two validation tests will be performed. For each test, the reduction of error (RE), coefficient of efficiency (CE), and the coefficient of determination (r2) will be obtained.

The RE statistic was first used by Lorenz (1956) to determine if a meteorological forecast (pressure reconstruction for the purposes of this thesis) was better than the climatological mean, such as the mean of the data during the validation period. The RE is calculated as follows (Equation 3.5):

∑ ̂ [ ] (3.5) ∑

where xi and ̂ are the actual and estimated data during the verification period. The verification period is either over 1957-2011, or one of two separate 25-year periods

(1987-2011 or 1957-1981) depending on the validation technique employed, as described in detail later. is the mean of the actual data in the calibration period (again, either the full 1957-2011period or one of two separate 30-year periods, 1957-1986 or 1982-2011, depending on the validation scheme employed). The RE value ranges between -∞ and

+1.0, with a negative value indicating a model performance that is not better than using the climatology mean. Hence, a positive result represents a better result of reconstruction.

The CE statistic was first introduced by Nash and Sutcliffe (1971) in a hydrology study and later adopted by Briffa et al. (1986) in their dendroclimatology study. The CE is calculated as follows (Eq. 3.6):

∑ ̂ [ ] (3.6) ∑

where xi and ̂ are the actual and estimated data during the verification period that are the same as the RE method. The only difference is the denominator that is the mean of the actual data in the verification period. The CE test is similar to the RE test in that it has a theoretical range between -∞ and +1.0. A positive value indicates that it is better than verification period climatology and a negative value indicates that it is less skilled.

As the CE test is more difficult to pass, the CE value is always less than the RE value. 55

These values will determine the overall uncertainty of the reconstructions. The best model (number of retained PCs, correlation significance, etc.) will be determined based on the highest values of the RE and CE from the validation tests, as well as the most complete explanation of variance during the verification period. In order to verify the skill of the reconstruction, two validation procedures will be performed as described in the following sections.

3.3.1 Leave-one-out Cross Validation (Calibration and Validation period 1957-2011)

This method is similar to that employed by Jones et al. (2009) and uses the full period 1957-2011 for the model calibration. The PCR reconstruction method will be performed on the same number of years as the calibration period (1957-2011, or 55 times in this case; Fig. 3.3). Each individual year and its two neighboring years (total 5 years; yellow and green color boxes in Fig. 3.3) are left out to ensure the independence of the center year; the value for this „missing‟ year (yellow box) is then determined using PCR on the other remaining years (pink boxes). After the PCR has run 55 times, each individual year will be concatenated to produce a full validation time period (bottom row). This method will produce calibration correlation, validation correlation, RE, and

CE values. Uncertainty in the original reconstruction will then be determined by overlapping both the calibration and validation series.

Figure 3.3: A diagram of the leave-one-out cross validation method. The green boxes are the years that are left out; the yellow boxes are the estimated years; the pink boxes are the years that are used in PCR. There are a total of 55 time steps in this thesis and all the estimated years will be collected in the final step.

3.3.2 30-year Calibration Period and 25-year Validation Period

This approach is adopted from Steig et al. (2009) in their surface temperature reconstruction. The PCR will be again performed with different calibration and validation periods, as shown in Fig. 3.4. The calibration series of the PCR model is divided into two periods with the first 30 years (1957-1986) and the last 30 years (1982-2011); the remaining 25 years (1987-2011 and 1957-1981) are left for validation. Comparing the two results effectively tests the PCR model sensitivity to the temporal variation during the calibration period. For instance, similar results (calibration and validation correlations) from two different calibration periods can imply the model is not sensitive with the time period. A comparison of the full calibrated period of record (1957-2011) can also explain the stability and reliability of the reconstruction model. This method will 57 produce two sets of calibration correlation, validation correlation, RE, and CE values, as it will perform twice with different calibration periods (1957-1986 and 1982-2011).

Figure 3.4: Procedure of the 30-year calibration and 25-year validation period method. The first blue box on the left is the calibration periods and the middle blue box is the validation periods in this study.

CHAPTER 4: PRESSURE RECONSTRUCTIONS

In order to have a better understanding of the historical station pressures in

Antarctica, it is essential to have long-term reliable records. As discussed in Chapter 1, a few attempts have been made to reconstruct the Antarctic pressure for the entire 20th century. One of the examples is the 20th Century Reanalysis (20CR) project (Compo et al.

2011). However, the product is not purely from observations, and is instead guided by surface pressure, sea surface temperature, and sea ice conditions. Additionally, another attempt is the Hadley Centre gridded mean sea level pressure (HadSLP2; Allan and

Ansell 2006). Nevertheless, it has deficiencies in the data-sparse regions and has suffered from coarse resolution, with only a 5x5 latitude-longitude degree resolution. Therefore, it is necessary to acquire a more accurate historical record by using a different approach that contains much less noise and greater certainty.

The new station-based pressure reconstruction technique - Principal Component

Regression (PCR) - has been detailed in Chapter 3. This chapter evaluates these station- based pressure reconstructions employing the PCR methodology, with only Southern

Hemisphere midlatitude pressure observations used as predictors.

4.1 Overall pressure reconstruction result

Pressure reconstructions for 18 Antarctic stations (Table 3.1) have been performed over the period of 1905-2011, with model calibration typically starting in 1957

(except for Marambio station, where observations do not begin until 1970). To assess the uncertainty and accuracy of the PCR model, two validation tests have been used. The first one is a leave-one-out cross validation test. In this approach, the full period of 1957-2011 59 is used for the model calibration and validation. The second approach calibrated separately the PCR model to the first 30 years (1957-1986) and last 30 years (1982-

2011). The remaining 25 years in each calibration were used as an independent validation period. Hereafter, „full period‟ refers to the reconstructions performed using the leave- one-out cross-validation technique; „early period‟ refers to the reconstructions that were calibrated during 1957-1986 and validated during 1987-2011; finally, „late period‟ refers to reconstructions that were calibrated during 1982-2011 and validated during 1957-

1981. The entire reconstruction procedure was repeated for two station predictor networks: one each where stations are included if they were significantly correlated with the predictand at p<0.05 and p<0.10, as shown in Table 3.2 (hereafter termed „p<0.05 network‟ and „p<0.10 network‟). For each approach, the reduction of error (RE), coefficient of efficiency (CE), and the coefficient of determination (r2) were calculated as a straightforward statistical means of evaluating reconstruction performance.

Table 4.1 and Fig. 4.1 present the correlations between the reconstructions and the observed pressure values at each station during the calibration period (i.e., the

„calibration correlations‟) in each season, for both the p<0.05 and p<0.10 predictor networks. In this research, calibration correlations greater than 0.6 or 0.8 in both predictor networks are shown in Table 4.1. Notably, during the calibration period, the reconstructions and the observed pressure values show the strongest relationship in austral summer (DJF), with a minimum of 17/18 stations for all periods with r>0.6. In

JJA, there are at least 15 stations with r>0.6 except the late period, which is slightly less with a minimum of 12 stations. The number of stations with calibration correlations of 60 r>0.6 are slightly smaller in austral autumn (MAM) and spring (SON), yet still contain about 10 stations in the full and late periods, with the only exception in the early SON period, when 7 stations are in the p<0.05 network and 6 stations in the p<0.10 network.

Even though the number of stations of r>0.08 is significantly reduced compared to r>0.6, the number of stations that have r>0.7 is still relatively high (data not shown), especially in DJF. Nevertheless, DJF and JJA still have a higher number of stations that have r>0.8 than MAM and SON.

Table 4.1: Number of stations where correlations between the reconstruction and observed values are larger than 0.6 and 0.8 respectively, during the calibration period. The total number of stations is 18 and percentage is shown in parentheses.

Calibration Correlation p<0.05 network p<0.10 network Period r > 0.6 r > 0.8 r > 0.6 r > 0.8 DJF full 18 (100) 6 (33) 18 (100) 5 (28) early 17 (94) 12 (67) 18 (100) 13 (72) late 17 (94) 6 (33) 17 (94) 5 (28) MAM full 11 (61) 1 (6) 10 (56) 1 (6) early 9 (50) 2 (11) 8 (44) 2 (11) late 9 (50) 1 (6) 10 (56) 3 (17) JJA full 15 (83) 4 (22) 17 (94) 5 (28) early 17 (94) 5 (28) 17 (94) 7 (39) late 13 (72) 6 (33) 12 (67) 6 (33) SON full 10 (56) 1 (6) 13 (72) 1 (6) early 7 (39) 2 (11) 6 (33) 2 (11) late 13 (72) 0 (0) 13 (72) 1 (6)

Figure 4.1: Histogram plot of the calibration correlations in each season from the p<0.05 and p<0.10 predictor networks (red = p<0.05 network, r>0.6; sky blue = p<0.05 network, r>0.8; green = p<0.10 network, r>0.6; and purple = p<0.10 network, r>0.8).

Figure 4.2 shows the spatial distribution of the calibration correlations in each season. In each station, the best-reconstructed result has been selected from the two predictor networks. Among all seasons, stations that are located in the Antarctic

Peninsula, such as Marambio and Faraday, tend to have higher calibration correlations than East Antarctic stations. This is due to the nearby predictor station Orcadas, which resides off the tip of the Antarctic Peninsula (60.7°S, 40.7°W), and is highly correlated with these stations (and whose observational record extends back until 1903). In East

Antarctica, Dumont d‟Urville station also has high calibration correlations in all seasons, whereas stations like Syowa and Mirny are relatively lower. Importantly, even though

Byrd station data was heavily patched, it still has fairly high calibration correlations in all seasons. 62

Figure continues on next page 63

Figure 4.2: Seasonal maps of the calibration correlation magnitude for each Antarctic station that was reconstructed, with the best reconstructions chosen from the two predictor networks. The Antarctic Peninsula stations are enlarged in the square boxes on the left of each map. (grey = 0.3

The validation correlations (correlations between the validation series and the reconstruction at each station, during the validation period) are presented in a similar fashion to the calibration correlations in Table 4.2 and Fig. 4.3; as noted earlier these validation correlations provide an independent measure of the accuracy and uncertainty between the PCR model and the observed data (predictand). At least 90% of the stations

(16/18 stations) have a validation correlation of 0.4 or higher, except the late period in

SON (13/18; 72%). In DJF, all the stations (18/18) have validation correlations larger than 0.4, and overall this season continues to have the highest number of stations in all periods with validation correlations r>0.6, with a majority of 16 stations (89%). Notably, in DJF the late period reconstruction does exceptionally well compared with the other seasons, with 12/18 and 13/18 stations validation correlations that are r >0.8 in the p<0.5 and p<0.10 predictor networks, respectively. Examining the calibration correlations in

DJF, it is also apparent that the early period reconstruction produces notably higher calibration correlations. Together, this suggests that the PCR model is very effective at capturing Antarctic pressure variability in DJF during the earlier period (roughly from

1957-1986), but the model skill has declined somewhat over the last 25 years.

In JJA, the validation correlations are lower than DJF, but the reconstructions still capture the variability reasonably well, with more than 10 stations having validation correlations larger than 0.6 for all reconstruction periods. In MAM, the PCR model is better at reproducing the pressure data in early and late periods than in the full period, meaning that perhaps the surface pressure teleconnections to the mid-latitudes might change from the periods of 1957-1981 to 1987-2011, causing lower validation values in 65 the longer period reconstruction (full period). For validation correlations of r>0.8 in

SON, again generally the same situation can be seen: that stations have higher validation correlations in early and late period reconstructions than in the full period reconstruction.

Spatial plots of the validation correlations by season are shown in Fig. 4.4. Stations in the

Antarctic Peninsula like Faraday station continue to present the highest concentration of high validation correlations. In East Antarctica, however, coastal stations, such as Syowa and Mirny, tend to have weaker validation correlations in general. Nevertheless, the best station pressure reconstructions from the p<0.5 and p<0.10 predictor networks have minimum validation correlation values of 0.4 or higher.

Table 4.2: Number of stations where correlations between the reconstruction and observed values are larger than 0.4, 0.6 and 0.8 respectively, during the validation period. The total number of stations is 18 and percentage is shown in parentheses.

Validation correlation p<0.5 p<0.10 Period r > 0.4 r > 0.6 r > 0.8 r > 0.4 r > 0.6 r > 0.8 DJF full 18 (100) 16 (89) 1 (6) 18 (100) 16 (89) 1 (6) early 18 (100) 16 (89) 3 (17) 18 (100) 14 (78) 2 (11) late 18 (100) 18 (100) 12 (67) 18 (100) 18 (100) 13 (72) MAM full 17 (94) 6 (33) 0 (0) 18 (100) 7 (39) 0 (0) early 17 (94) 11 (61) 1 (6) 17 (94) 12 (67) 2 (11) late 16 (89) 12 (67) 3 (17) 16 (89) 13 (72) 2 (11) JJA full 18 (100) 10 (56) 4 (22) 18 (100) 11 (61) 4 (22) early 16 (89) 12 (67) 4 (22) 16 (89) 13 (72) 4 (22) late 18 (100) 15 (83) 4 (22) 17 (94) 14 (78) 5 (28) SON full 18 (100) 3 (17) 0 (0) 17 (94) 6 (33) 0 (0) early 18 (100) 14 (78) 3 (17) 18 (100) 13 (72) 2 (11) late 13 (72) 8 (44) 3 (17) 15 (83) 8 (44) 3 (17) 66

Figure 4.3: Histogram plot of the validation correlations in each season from the p<0.5 and p<0.10 predictor networks (red = p<0.5 network, r>0.4; skyblue = p<0.5 network, r>0.6; green = p<0.5 network, r>0.8; purple = p<0.10 network r>0.4; darkgreen = p<0.10 network, r>0.6; and orange = p<0.10 network, r>0.8). 67

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Figure 4.4: Seasonal maps of the validation correlation magnitude for each Antarctic station that was reconstructed, with the best reconstructions chosen from the two predictor networks. The Antarctic Peninsula stations are enlarged in the square boxes on the left of each map. (grey = 0.3

Table 4.3 and Fig. 4.5 depict the statistic of reduction of error (RE) for the pressure reconstructions. RE is used to determine if the pressure reconstruction is better than the climatological mean (during the calibration period): a positive value indicates the model performance is better than simply using the calibration period mean, and value of

1 indicates a perfect reconstruction (the reconstruction perfectly follows the observed record during the calibration period). DJF does exceptionally well in that nearly all stations have a RE that is 0.4 or higher. Moreover, 16 stations display a RE that is 0.6 or higher for the full and late period reconstructions (with the exception of the late period reconstruction using the p<0.5 predictor network), indicating that more predictors encompassed in the DJF p<0.10 predictor network might contribute to higher RE values in the late period reconstruction. In MAM, out of the 18 reconstructed stations, at least

17 stations have a RE>0.2, regardless of reconstruction period or predictor network, suggesting nearly all stations are able to be reconstructed at a skill higher than using the calibration mean; as few as 10 and as many as 15 stations have RE larger than 0.4 in

MAM. Even though there are relatively few stations outside of austral summer that have

RE>0.6, with 6 stations or less (the highest percentages being in JJA), the majority of stations still have a RE>0.2 regardless of season, and 50% or more of the stations have a

RE of 0.4 or greater (except for the late period reconstructions in SON). In SON, the late period reconstructions display relatively lower skill, with only 6 stations in both networks having RE>0.6, indicating the PCR has less skill in reproducing the calibration period in

1982-2011. 70

In terms of the spatial distribution of RE, Antarctic Peninsula stations like

Bellingshausen and Faraday have the highest reconstruction skills, as shown in Fig. 4.6.

Stations in East Antarctica tend to have lower RE, especially in MAM and SON.

Interestingly, in DJF, high values of RE can even be seen in East Antarctica, with Halley and Novolazarevskaya as great examples that display considerably higher RE values in austral summer than in other seasons.

Table 4.3: Number of stations where reduction of error (RE) is larger than 0.2, 0.4, and 0.6, respectively. The total number of stations is 18 and percentage is shown in parentheses.

RE p<0.5 p<0.10 Period > 0.2 > 0.4 > 0.6 > 0.2 > 0.4 > 0.6 DJF full 18 (100) 18 (100) 16 (89) 18 (100) 18 (100) 16 (89) early 18 (100) 17 (94) 10 (56) 18 (100) 18 (100) 7 (39) late 18 (100) 18 (100) 11 (61) 18 (100) 18 (100) 16 (89) MAM full 18 (100) 14 (78) 4 (22) 18 (100) 13 (72) 5 (28) early 18 (100) 10 (56) 2 (11) 17 (94) 12 (67) 3 (17) late 17 (94) 15 (83) 4 (22) 17 (94) 15 (83) 4 (22) JJA full 18 (100) 14 (78) 6 (33) 18 (100) 16 (89) 6 (33) early 14 (78) 9 (50) 4 (22) 14 (78) 9 (50) 4 (22) late 16 (89) 13 (72) 5 (28) 17 (94) 12 (67) 5 (28) SON full 17 (94) 9 (50) 1 (6) 16 (89) 12 (67) 2 (11) early 17 (94) 9 (50) 1 (6) 17 (94) 10 (56) 1 (6) late 12 (67) 6 (33) 2 (11) 14 (78) 6 (33) 3 (17)

Figure 4.5: Histogram plot of the reduction of error (RE) in each season from the p<0.5 and p<0.10 predictor networks (red = p<0.5 network, r>0.2; skyblue = p<0.5 network, r>0.4; green = p<0.5 network, r>0.6; purple = p<0.10 network r>0.2; darkgreen = p<0.10 network, r>0.4; and orange = p<0.10 network, r>0.6). 72

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Figure 4.6: Seasonal maps of the reduction of error (RE) magnitude for each Antarctic station that was reconstructed, with the best reconstructions chosen from the two predictor networks. The Antarctic Peninsula stations are enlarged in the square boxes on the left of each map. (grey = 0.2 or lower; yellow = 0.2-0.4; green = 0.4-0.6; blue = 0.6- 0.8; and red = 0.8-0.9). 74

The coefficient of error (CE) is another method used to understand the accuracy of the reconstruction model. It is similar to the RE in that it has a range between -∞ and

+1.0, yet the CE value is more difficult to pass as the calculation uses the mean of the actual data in the verification period. Hence, CE must be either the same or lower than

RE in each case, as it is tested against an independent mean (during the validation period). The values of CE for the reconstructions are displayed in Table 4.4 and Fig. 4.7.

Overall in each season more than half of the stations have a CE>0.2 for both the p<0.5 and p<0.10 predictor networks. Among the seasons, DJF has the best result with at least

17/18 (94%) stations in all periods that are larger than 0.2. In particular, DJF full and late period reconstructions have the highest number of stations whose CE is larger than 0.6, with 12 stations in both predictor networks for the full period and 9/18 and 13/18 in late period for the p<0.05 and p<0.10 networks, respectively. In contrast, there are 2 stations or less for either predictor network at which CE is larger than 0.6 in DJF for the early period reconstruction, indicating that the early PCR model in DJF has relatively lower skill based on this statistic. Similar situations can be seen in other seasons, with a large portion of stations having CE values larger than 0.2, yet less than 10 stations have CE values larger than 0.4. Overall, even though most of the stations do not have high CE values, a majority of stations are still able to produce positive CE values (refer to

Appendix), meaning the reconstructions are still passing this more rigorous test, and are performing with better skill than would be attained simply using the validation period mean. Examining the spatial maps of CE (Fig. 4.8), Antarctic Peninsula stations continue to have higher CE values in general, whereas stations in East Antarctica tend to have 75 lower CE values. Interestingly, stations between 100°E-180°E generally have higher values in DJF and JJA compared to other seasons.

Table 4.4: Number of stations where coefficient of efficiency (CE) is larger than 0.2, 0.4 and 0.6, respectively. The total number of stations is 18 and percentage is shown in parentheses.

CE p<0.05 p<0.10 Period > 0.2 > 0.4 > 0.6 > 0.2 > 0.4 > 0.6 DJF full 18 (100) 18 (100) 12 (67) 18 (100) 18 (100) 12 (67) early 17 (94) 9 (50) 2 (11) 18 (100) 9 (50) 0 (0) late 17 (94) 15 (83) 9 (50) 18 (100) 14 (78) 13 (72) MAM full 17 (94) 7 (39) 1 (6) 17 (94) 7 (39) 2 (11) early 17 (94) 4 (22) 1 (6) 17 (94) 4 (22) 2 (11) late 14 (78) 8 (44) 4 (22) 15 (83) 8 (44) 4 (22) JJA full 17 (94) 8 (44) 5 (28) 18 (100) 8 (44) 5 (28) early 11 (61) 7 (39) 4 (22) 11 (61) 7 (39) 4 (22) late 16 (89) 10 (56) 4 (22) 16 (89) 10 (56) 4 (22) SON full 12 (67) 4 (22) 1 (6) 13 (72) 5 (28) 1 (6) early 13 (72) 5 (28) 1 (6) 14 (78) 7 (39) 1 (6) late 10 (56) 5 (28) 2 (11) 11 (61) 4 (22) 3 (17)

Figure 4.7: Histogram plot of the coefficient of efficiency (CE) in each season from the p<0.5 and p<0.10 predictor networks (red = p <0.5 network, r>0.2; skyblue = p<0.5 network, r>0.4; green = p<0.5 network, r>0.6; purple = p<0.10 network r>0.2; darkgreen = p<0.10 network, r>0.4; and orange = p<0.10 network, r>0.6).

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Figure 4.8: Seasonal maps of the coefficient of efficiency (CE) magnitude for each Antarctic station that was reconstructed, with the best reconstructions chosen from the two predictor networks. The Antarctic Peninsula stations are enlarged in the square boxes on the left of each map. (grey = 0.2 or lower; yellow = 0.2-0.4; blue = 0.4-0.6; and red = 0.6-0.8).

Concerning all the verifications tests performed among each season, a large percentage of the Antarctic station-based pressure reconstructions demonstrate modest

(and sufficient) skill (i.e, the majority of the reconstructions have RE and CE values that are larger than 0.2, and validation correlations greater than 0.4; Appendix). In particular, the austral summer (DJF) pressure reconstruction has the highest skill compared with other seasons. JJA is slightly weaker, yet still able to reproduce reconstructions with fairly low uncertainty. The skill is lowest in MAM and SON, but still performing better than climatology. The reconstruction performance varies spatially as well, with stations that are located in the Antarctic Peninsula generally having the highest verification measures, whereas coastal stations in East Antarctica tend to have lower measures of reconstruction skill.

4.2 Trend validation

In order to examine performance of the station pressure reconstructions, it is also important to compare the trends between the station observations and the reconstructions at each location, as this is another way to examine the reconstruction skill. Since the reconstruction model was based on detrended data, similar trends in both observation and reconstruction increase the confidence in high reconstruction skills (the trends are solely produced by the statistical relationships between the predictors and predictand, not on the trends that exist within each). This section examines the linear trends between the observed record and the best reconstruction record (either p<0.05 or p<0.10 networks), starting at the first observed year (which varies by station; Table 3.1.). It is nevertheless important to keep in mind that the goal in this section is to examine if stations have 80 similar trends in observation and reconstruction during the full period of overlap. The uniqueness of the trends will be discussed in-depth in Chapter 5, when the entire reconstruction period will be examined.

Figures 4.9a-d shows the observation and reconstruction trends in all stations along with the 95% confidence intervals. In DJF, overall the reconstruction trends capture the observed trends very well (Fig. 4.9a), as a large portion of the reconstruction confidence intervals are overlapped with the uncertainty in the observed trends.

Nevertheless, trends in three stations have more notable differences than the others (large confidence interval ranges; namely Byrd, Marambio, and Marsh/O‟Higgins), though a part of the 95% confidence intervals still overlap the uncertainty in the observed trends in each case. At Byrd, the differences in the trend magnitude are quite large, with the observed trend being 0.071±0.666 hPa decade-1, whereas the reconstruction trend is negative (-0.7±0.449 hPa decade-1). These larger differences are primarily result from missing years in the observed record that are not part of the reconstruction; the observed record is only 76% complete at Byrd. For Marambio and Marsh/O‟Higgins, both stations have negative trends in the observed record and close to zero in the reconstruction record.

This difference in trends is related to the late observation starting period, where

Marambio began in 1970 and Marsh/O‟Higgins began in 1963. Interestingly, nearly all the reconstructions have statistically significant (p<0.05 or p<0.10 predictor networks) negative trends, whereas most of the observed trends are not significantly different from zero, as the 95% confidence intervals cross the zero line. 81

During MAM (Fig. 4.9b), the majority of the reconstruction confidence intervals still overlap with the observed records. Nevertheless, there are a few stations with fairly large differences, where the reconstructed trends are either slightly positive or negative, and statistically significant at p<0.05 or p<0.10 (i.e., Amundsen Scott, Casey, Dumont d‟Urville, Rothera, and Vostok). In general, station trends in austral autumn tend to vary at each station.

Figure 4.9: Observed trends (black) and the best reconstruction trends (red) in 1957-2011 (DJF 1957-2010) for a) summer, b) autumn, c) winter, and d) spring. Error bars represent 95% confidence intervals (maximum and minimum values). In each station (from left to right on the x-axis), the observation trend is shown first, with the reconstruction trend listed second.

During JJA, nearly all of the reconstruction trends capture the observed trends

(Fig. 4.9c). In particular, only Byrd has signs of a different trend, with a positive observed station pressure trend of 0.861±0.774 hPa decade-1 and a negative station pressure reconstruction trend of -0.471±0.433 hPa decade-1, in which case the trend differences are again generated by missing years in the predictand. Moreover, during the 83 austral winter, both observed and reconstruction trends are not statistically significant at the p<0.05 or p<0.10.

In SON (Fig. 4.9d), generally a larger difference is noted between the observed and reconstruction trends, yet the 95% confidence intervals around both trends still overlap at most of the stations. In particular, Byrd has a large trend difference, where the observed trend is strongly positive at 1.083±0.661 hPa decade-1 and the reconstruction trend is slightly negative at -0.398±0.363 hPa decade-1. Nevertheless, missing 16 predictand years might play a significant role in inducing a positive trend during the austral spring. Other stations, such as Amundsen Scott and Vostok, appear to have less agreement between the observed and reconstruction trends, including different signs in the trend magnitude.

Overall, the reconstruction trends capture the observed trends well, especially in

DJF and JJA, with many trends nearly identical. In contrast, based on this metric, MAM and SON display less reconstruction skill than the other seasons, although the trends are not statistically different from one another. These findings are consistent with the fact that the previous section has shown that DJF and JJA have higher reconstruction skill scores (i.e., validation correlation, RE, and CE) in general. Notably, the results of those stations that have larger uncertainty between the observed and reconstruction trends, such as Byrd, are mainly caused by missing years, which strongly influences the calculation of the linear trends, and leads to a weak agreement with the observed trend. Nevertheless, in comparing the trends between the observed and reconstruction records, it is believed that 84 the reconstructions are still considered to be reliable and generally produce similar changes in the pressure at each station during 1957-2011.

4.3 Station-based pressure reconstruction

As the overall reconstruction performance across the 18 stations has already been shown and discussed, it is prudent to investigate the pressure reconstruction individually at a few key stations. Stations that have high or low reconstruction skills, based on individual validation metrics or large differences between validation methods are investigated further in detail to understand why these differences in reconstruction performance arise.

4.3.1 High-skill pressure reconstruction stations

Faraday (65.4°S, 64.4°W, Station ID: 890630)

Faraday is a coastal station that is located at the northern Antarctic Peninsula with an elevation of 11m above sea level. It is one of several stations that did remarkably well in all validation tests. The station began collecting meteorological observations in 1950, and the pressure record at Faraday is 99.5% complete during the period of 1950-2011. In

DJF (Fig. 4.10a), both full period p<0.05 and p<0.10 predictor networks have remarkable results, (validation correlations ≥ 0.80), with the larger p<0.10 predictor network performing slightly better than the p<0.05 network (for p<0.10, validation correlation=0.802; RE=0.782; CE=0.699). This can be explained by one important predictor - Orcadas (60.7°S, 44.7°W). Fig. 4.11 shows the beta weight map distribution of Faraday pressure reconstruction in all seasons (from the p<0.10 predictor network), in which Orcadas has a high beta weight (0.588) and correlation (0.767) with Faraday in 85

DJF, meaning Orcadas is the most important predictor in Faraday pressure reconstruction. Moreover, Fig. 4.10a clearly shows that the DJF reconstruction in

Faraday captures the observation data very well during the 1957-2010 period, with the only exception being that the DJF reconstruction does not reproduce the peak seen in

2003. Across the various validation series, there is about a 0.09 validation correlation difference between early and late periods for both p<0.05 and p<0.10 predictor networks.

In this case, the late period reconstruction has been captured better than the early reconstruction.

Figure 4.10: Reconstructions at Faraday station during 1957-2011, which represents the best full period station pressure reconstruction and observed record among all seasons. The observed record is in black while the reconstructed record is in red, with the 95% confidence intervals of the reconstruction shaded in grey.

In MAM, a slightly weaker but still the third-best pressure reconstruction in all seasons was gathered using the full period for model calibration. This approach produces validation correlations larger than 0.7 in both predictor networks. In particular, the p<0.05 predictor network has a better result (Fig. 4.10b), with a validation correlation=0.743, RE=0.643, and CE=0.576. In Fig. 4.10b, the reconstruction portrays fairly well as it captures the major peaks/troughs, with an exception in 2006-2007, where the reconstruction displayed opposite variability than observed. Even though Faraday only has 4 predictors in austral autumn, Orcadas continues to play an important role with beta weights (0.674) and correlation (0.77) in the p<0.10 predictor network (Fig. 4.11).

Comparing the validation approaches, the late periods perform better than the early periods with validation correlation at about 0.8, which can be explained by the stronger observed pressure correlation with the first EOF from the predictor network. This implies that the predictor network explains majority of the variability at Faraday, and that the first

EOF of this predictor network is an important pattern related to the temporal variability at

Faraday as well.

Among all seasons, the JJA pressure reconstruction at Faraday has the highest skill. In general, both reconstructed and observed records match very well from 1957-

2011 except in 1975, where the peak is not depicted in the reconstructed record (Fig.

4.10c). Moreover, both full period predictor networks have validation correlations higher than 0.84. In the p<0.10 predictor network, Orcadas once again has the highest beta weight (0.622; Fig. 4.11) and correlation (0.752), which again indicates the importance of this predictor. Moreover, the other two validation procedures produced similar results 87 with a validation correlation at about 0.8. In SON, only one pressure reconstruction was produced as both networks have the same number of predictors. It again has an excellent result with validation correlation=0.721, RE=0.589, and CE=0.555 for the full period reconstruction (Fig. 4.10d). Even though there are only three predictors, Orcadas continues to have high beta weight values (0.498; Fig. 4.11) and correlation with the observed pressure at Faraday (0.683). In general, the reconstructed record captures the peaks and troughs well, even though the observed record has a larger trough in some years, such as 1993 and 1996. In terms of the validation sensitivity tests, there are no notable differences in the various validation approaches in SON.

Figure continues on next page 89

Figure 4.11: Seasonal maps of beta weight magnitude for each predictor station at Faraday from the p<0.10 predictor network. Note that SON has the same number of predictors in both p<0.5 and p<0.10 predictor networks (yellow = 0-0.2; green = 0.2-0.4; blue = 0.4- 0.6; and red = 0.6-0.7). 90

Bellingshausen (62.2°S, 58.9°W, Station ID: 890500)

Bellingshausen is another station situated at the northern Antarctic Peninsula

(16m above sea level) that also produces a reliable and skillful reconstruction across all seasons. The station started collecting meteorological data in 1959, but its record was patched with two nearby (less than 10 miles away) stations to extend the record to 2011 with 99.9% data completeness. Only one reconstruction in DJF was produced as both the p<0.5 and p<0.10 networks have the same number of predictors. The full period DJF reconstruction is well-produced, with validation correlation=0.733, RE=0.761, and

CE=0.652. Fig. 4.12a shows that the reconstructed record portrays the observed data very well, with an exception of the 2000-2005 period, where the observed record minima and maxima appear to be stronger. Although only 44 out of 54 years were used in the PCR model (due to years with missing data in the predictor stations), station Orcadas has the largest beta weight (0.598) and highest correlation (0.74) among all predictors, producing a skillful reconstructed record (Fig. 4.13a). In regards to the validation sensitivity tests, results from early and late reconstruction periods agree well, with validation correlation of 0.682 in the early period and validation correlation of 0.688 in the late period; these similarities show very little sensitivity to the time period over which the reconstruction model is calibrated in DJF at Bellingshausen. 91

Figure 4.12: Reconstructions at Bellingshausen station during 1957-2011, which represents the best full period station pressure reconstruction and observed record among all seasons. The observed record is in black while the reconstructed record is in red, with the 95% confidence intervals of the reconstruction shaded in grey.

In MAM, the Bellingshausen pressure reconstruction has the highest skill among all stations. Both pressure reconstructions in the full period have validation correlations that are larger than 0.77, with validation correlation=0.796, RE=0.744, and CE=0.676 in the p<0.10 predictor network. The maxima and minima from the observed record are well depicted by the reconstructed record (Fig. 4.12b), although there are a few years during which the observed record is higher in magnitude (i.e., 1961, 1968, and 1981).

Nevertheless, the PCR model still generates a reliable Bellingshausen pressure reconstruction in MAM. This is again related to Orcadas station, which has a high beta weight (0.637; Fig. 4.13) and correlation with Bellingshausen observed pressure (0.795). 92

In terms of the validation sensitivity tests, the validation correlations are about 0.8, which have values similar to the first validation test.

In JJA, the Bellingshausen full period reconstruction once again performed exceptionally high in the full period validation, with validation correlation=0.885,

RE=0.837, and CE=0.776 (both tests). In this case, Orcadas has a high beta weight

(0.739; Fig. 4.13) and high correlation (0.856) in the PCR model. In comparison with the observed and reconstructed records, the reconstructed record matches the maxima and minima of the observed record very well in general, as shown in Fig. 4.12c. Relating to the validation sensitivity tests, it also has similar results with the full period validation in that the validation correlations are close to 0.9.

In SON, Bellingshausen repeats its remarkable pressure reconstruction in the full period, with validation correlation=0.758, RE=0.733, and CE=0.652 (both tests; Fig.

4.12d). Despite the fact that there were only four predictors in the model, Orcadas station contributes the highest beta weight (0.681; Fig. 4.13) and high correlation with

Bellingshausen (0.791). Fig. 4.12d shows that the reconstructed record portrays the observed record very well, with the only exception being that the reconstructed record cannot capture the peak in 1982. In terms of the validation sensitivity tests, they produce a great result with validation correlations at about 0.9. Such results are related to the strong pressure correlation and high percent of variance explained by the first EOF from the predictor network.

Figure continues on next page 94

Figure 4.13: Seasonal maps of beta weight magnitude for each predictor station at Bellingshausen from the p<0.10 predictor network. Note that DJF, JJA, and SON have the same number of predictors in the p<0.5 and p<0.10 predictor networks (yellow = 0-0.2; green = 0.2-0.4; blue = 0.4-0.6; and red = 0.6-0.8). 95

As noted earlier, the best reconstruction performance consistently across all seasons is across the Antarctic Peninsula. Specifically, these stations are close to the station – Orcadas, which constrains the model through high correlation with this predictor, ultimately producing strong beta weight values for the reconstruction. Notably, austral summer (SON) and winter (JJA) pressure reconstructions appear to have slightly better performances than other seasons, which might be due to the fact that austral summer and winter are marked with less variability and stronger connection to the midlatitudes than during austral spring and autumn, when the Southern Atlantic Oscillation is at its greatest amplitude and the Antarctic climate is transitioning to and from prolonged periods of sunlight.

4.3.2 Low-skill pressure reconstruction stations

In order to understand the pressure reconstruction performance from the PCR model, it is also important to interpret and understand the reasons for weak performance in certain stations. Here two stations whose reconstructions demonstrate lower skills are discussed further, namely Byrd, and Syowa.

Byrd (80°S, 119.4°W, Station ID: 893240)

Byrd is one of the few inland Antarctic stations that are located in West

Antarctica, with an altitude 1530m above sea level. Byrd is an automatic weather station

(no permanent establishment exists currently) that first started meteorological observations in 1980. In order to acquire a longer record, the AWS record has been extended back in time with four nearby automatic and staffed stations (including a historical staffed station similarly named „Byrd‟). The data from these records start in 96

1957, although even with patching the full record is only 76% complete through 2011.

Moreover, due to the fact that it is missing years itself, the PCR model has a relatively low number of years used to determine the relationships between the predictand and the predictor stations among all seasons, which provides fairly weak results in general. The exception is in DJF, as during this season there are more predictor stations with a stronger correlation with Byrd. In DJF, the full period validation correlations are larger than 0.63 in two tests, with the best pressure reconstruction within the p<0.10 predictor network having validation correlation=0.677, RE=0.586, and CE=0.487. Although much of the observed record is missing after 1957, Fig. 4.14a shows that the reconstructed data is fairly successful at portraying the observed record. The largest deviation is that the peak in 1996 actually falls outside the 95% confidence intervals. In terms of the validation sensitivity tests, the models generate results of 0.67 or higher. The late period validation correlations display the best skill, with values of 0.88 or higher. This perhaps is related to more missing years in the first 25-year (1957-1981) period of the reconstructed record, making it easier to have a higher correlation with said record. 97

Figure 4.14: Reconstructions at Byrd station during 1957-2011, which represents the best full period station pressure reconstruction and observed record among all seasons. The observed record is in black while the reconstructed record is in red, with the 95% confidence intervals of the reconstruction shaded in grey.

The skill decreases markedly outside of austral summer. In MAM, the reconstruction at Byrd has the highest skill with the p<0.05 predictor network and calibrating the model over the full period, with validation correlation=0.537, RE=0.344, and CE=0.288. The lower skill is manifested also with three years of the observed data falling slightly out of the 95% confidence intervals of the reconstructed data (Fig. 4.14b).

They are 1962 (800.7hPa for observed vs. 807.1hPa for reconstruction), 1982 (802.6hPa vs. 808.9hPa), and 2007 (813.2hPa vs. 806.3hPa). Moreover, both 1982 and 2007 surface pressure records were from the observed record, while 1962 was patched from another station, and thus part of the error in reproducing the 1962 value may arise from how well 98 the patched record also represents that actual pressure variability at Byrd station.

Another important reason supporting the lower skill from the PCR model is due to missing 11 years (44/55) in the predictand and 8 years (47/55) in predictors, with small beta weights that are less than 0.14 in magnitude in all predictors, indicating no station had a strong relationship with the surface pressure in Byrd. Nevertheless, the reconstructed record is still capable of matching the variations (peaks and troughs) with the observed data. As in DJF, the late period validation sensitivity tests skill metrics are slightly larger, with validation correlation of 0.73 or higher (depending on the predictor network). This indicates that the last 30-year calibration period has done a better job of capturing the first 25 observation years than vice-versa.

In JJA, the skill of Byrd is even lower, with the best full period reconstruction

(p<0.10 predictor network) having a validation correlation=0.482, RE=0.385, and

CE=0.274, which again can primarily be explained by the total years of 41/55 and 46/55 in the predictand and predictors, respectively. In general, the pressure reconstruction captures the observed record fairly well, even though in some years the troughs or peaks appear to be stronger in the observed record. During this period, from 1957-2011, there is only one outlier outside the 95% confidence intervals of the reconstruction – 798hPa in observed vs. 805.8hPa in 1968 (Fig. 4.14c). Since this outlier only exists in non-patched recorded data, it increases the certainty that Byrd station pressure had a strong variation through time (nearly 20 hPa difference from 2009-2010 in the observed record).

Moreover, although it has a high beta weight of -0.764 in one predictor station (p<0.10 predictor network), the observed pressure correlation with that particular predictor still 99 appears to be comparably weak (-0.427). In regards to the validation sensitivity tests, results from early and late reconstruction periods have validation correlations 0.63 or higher. Hence, the lower skill of the reconstruction performance in the first validation test might arise from a large amount of missing predictand years (14/55).

In SON, a fair result is found with validation correlation=0.559, RE=0.225, and

CE=0.115 in p<0.05 full period predictor network (Fig. 4.14d). In fact, the PCR model only has a total of 39/55 valid years in observed years and 47/55 valid years in the predictor networks. During the SON, the reconstructed record appears to be slightly higher than the observed record before 1987, whereas a reverse finding is seen after 1987.

In this period, only one outlier is found that is outside the 95% confidence intervals of the reconstruction: 814.5hPa for observed vs. 805.7hPa for reconstructed in 1996. An overall reduction of valid predictand years might be able to explain low skill pressure reconstruction performance. In the validation sensitivity tests, the overall correlation values are higher than 0.6, with early period slightly better (>0.7). In this case, the large portion of missing predictand years (16/55) has had a great impact on weaker performance in the first validation test.

Even though the pressure reconstruction at Byrd might not be as good as the others, all validation metrics (RE, CE, validation correlation) clearly have positive values, indicating performance better than climatology. Furthermore, West Antarctica, especially the inland region, presents a unique challenge since none of the stations present have a long meteorological record, indicating that limited details about the overall pressure variability are known at present. Hence, in order to have more understanding of 100 the surface pressure in inland West Antarctica, the reconstructed pressure record of Byrd might still be considered as a useful and reliable source.

Syowa (69°S, 39.6°E, Station ID: 895320)

Syowa is another station that has a lower reconstruction skill. It is a coastal station in East Antarctica that has longer geographic distance from the predictors, as the closest predictors are in South Africa (Fig. 3.1). The meteorological observations at this station started in 1957, with 91% completeness through 2011. In DJF, both full period reconstruction tests have validation correlation less than 0.6, with the best reconstruction

(p<0.10 predictor network) displaying a validation correlation=0.595, RE=0.632, and

CE=0.576. In general, the reconstructed record performance does fairly well at capturing the observed period, in which only the peak in 1984 and the trough in 2004 cannot be reproduced by the PCR model (Fig. 4.15a). During the observed period, three outliers can be found – 1976 (995.8hPa for observed vs. 992.6hPa for reconstructed), 1999 (982.1hPa vs. 985.8hPa), and 2001 (982.4hPa vs. 987.3hPa) Moreover, the beta weights and correlations among the predictors are small, which help in explaining the lower reconstruction skill. Comparing the validation approach, the validation correlations of the early and late periods have a similar value (about 0.67) in the p<0.05 predictor network, indicating it is not time sensitive. 101

Figure 4.15: Reconstructions at Syowa station during 1957-2011, which represents the best full period station pressure reconstruction and observed record among all seasons. The observed record is in black while the reconstructed record is in red, with the 95% confidence intervals of the reconstruction shaded in grey.

In MAM, the Syowa pressure reconstruction has a lower reconstruction skill, with validation correlations values smaller than 0.5 in the full period validation. The p<0.10 predictor network has the best reconstruction with a validation correlation=0.493,

RE=0.399, and CE=0.331. The reconstructed record can capture the peaks and troughs of the observed record in general, although the observed record appears to be stronger in magnitude. Nevertheless, the model cannot capture the peaks from 1998-1992 (three peaks instead of one) and the trough in 1998 (Fig. 4.15b). During the observation period, there are a total of two outliers which fall outside the 95% confidence intervals of the 102 reconstructed series. They are 1982 (979.7hPa for observed vs. 985.6hPa), and 1990

(992.1hPa vs. 987.7hPa). Nevertheless, these years are all from direct observations (they have not been patched or are not missing years), indicating that reconstruction is unable to fully capture the extent of observed pressure variability at Syowa. Furthermore, the predictor correlations are small, which helps to explain the fairly weak reconstruction results. In regards to the validation sensitivity tests, they have slightly higher validation correlation values than when the model is calibrated over the full period, in which the early period has values of about 0.6, indicating that the first 30-year calibration period captured the last 25 years better in the PCR model.

In JJA, the Syowa pressure reconstruction has the lowest reconstruction skill among all seasons, where the p<0.05 predictor network is the best pressure reconstruction in the full period validation, with validation correlation=0.431, RE=0.361, and CE=0.187.

Although the reconstructed record is able to capture the peaks and troughs in the observed record, the reconstructed record seems to be stronger in magnitude (Fig. 4.15c).

In the observation period, 2004 and 2010 were the only outliers outside the reconstruction

95% confidence intervals, with the station pressure of 979.9hPa for observed vs.

985.6hPa in 2004 and 977.6hPa vs. 982.8hPa in 2010. The predictor correlations tend to be small in this season as well, with (absolute) values not exceeding 0.41. Furthermore, five missing observation years can also have influenced the performance of the Syowa

JJA reconstruction. In terms of the validation sensitivity tests, the p<0.05 predictor network is used with the validation correlations of about 0.43 in both periods, indicating the PCR model is not sensitive to the time period of model calibration. Moreover, the 103 pressure correlations between the predictors and predictand are again weak, with a majority of values less than 0.4, which also supports the reasoning behind weak reconstruction performances in JJA.

In SON, the p<0.05 predictor network calibrated over the full period has the validation correlation=0.55, RE=0.355, and CE=0.288. The reconstructed record is able to portray the peaks and troughs from the observed record, yet the observed record appears to have larger variability (Fig. 4.15d). During the observation period, two outliers can be found – 1988 (989.7 hPa) and 1997 (990.1 hPa). In terms of the validation sensitivity tests, the early period is about 0.23 higher than the late period, indicating the station pressure has been changing through time. Moreover, the small values of the predictors-predictand correlations further support the weak pressure reconstruction results.

Overall, Syowa pressure reconstruction in each season demonstrates consistently low skill. One reason for this is because five years are missing from the observed record.

Moreover, the geographical location might also play an important role, as all the predictors are a great distance from Syowa; other predictors might also exist away from permanent observation stations in the Southern Hemisphere (i.e., over ocean regions).

In summary, this section examined the station-pressure reconstructions by comparing them with the observed records during the observation periods. High-skill and low-skill stations (two of each) have been discussed in order to more specifically assess the PCR model. Particularly, low level reconstruction skills among the stations are 104 mainly caused by missing predictand years. Nevertheless, the reconstructed records are still capable of reproducing the majority of the peaks and troughs in the observed record.

4.3.3 Validation sensitivity test performance

To understand the overall performance among the stations, it is important to compare the validation correlation results from the two validation tests, especially the validation sensitivity tests between the early and late period, since similar validation correlations indicate the PCR model is not time-sensitive. Table 4.5 presents the validation correlation differences in both full period tests and validation sensitivity tests

(full, early, and late periods), in which differences between 0.2-0.3 are highlighted in yellow, and differences larger than 0.3 or higher in red. This section examines the validation correlation differences by season.

In DJF, the validation correlation differences appear to be small when comparing the full vs. early periods, in which none of the stations have differences higher than 0.2.

In contrast, large validation correlation differences can be seen when comparing the early vs. late periods, particularly at locations such as Davis and Novolazarevskaya, where differences are larger than 0.3 (Table 4.5a). The main reason for having large validation correlation differences (>0.2) among these stations seems to be missing years in the individual observations. For example, Marambio has 13 (14) missing observed years in the full (early) period test, causing the validation correlation differences to be larger than

0.3 in the full vs. late period and 0.2 in the early vs. late period, respectively. Moreover, as section 4.2 mentioned, Antarctic stations appear to have negative trends during the observed period in DJF. Hence, decreasing station pressure through time might perhaps 105 have a strong impact on validation correlations among stations, especially in the late period test; the predictor network in many cases produces slightly different trends than observed, which affects the overall correlation and validation metrics.

In MAM, the overall reconstruction performance appears to be less time-sensitive, as the majority of stations have small validation correlation differences (Table 4.5b). In this case, missing years in the observations appear to be the dominant reason for having larger validation correlation differences. Among the stations, Casey (full vs. late period in p<0.10 predictor network) and Mirny (early vs. late period in p<0.5 predictor network) have the largest validation correlation differences. Casey has a fairly low validation skill in general, at least partly explained by seven missing predictor years. At Mirny, there are more than five predictor years missing in the late period, coupled with a strong negative observed pressure trend after 1994, which further supports the large validation correlation differences with other periods.

The same situation can also be seen in JJA, where there are only a few stations that have validation correlation differences larger than 0.2 (Table 4.5c). In this case, the missing observed years can also be seen as a dominant factor among these stations. In particular, Byrd has 14 years missing in the observed record, leading to large validation correlation differences (>0.2 in most cases). Notably, Halley has the highest validation correlation differences among all seasons in JJA, with 0.33 in the early vs. late period validation, which is caused by a high pressure anomaly in 1964 (1001.3hPa). Hence, this anomaly significantly reduced the validation correlation in the early period. 106

During SON, more stations with higher validation correlation differences can be seen, in which the missing years in the observations continue to be the dominant factor in most cases (Table 4.5d). The second reason of differences between the validation approaches appears to be missing data for several years from the predictor stations.

Notably, a majority of the stations that have validation correlation differences (>0.3) can be supported by the lower number of valid predictor years used in the PCR model. For example, Davis has eight missing predictor years in the late period test, leading to a fairly weak validation correlation, which explains the reason behind large validation correlation differences when comparing the early vs. late periods.

Overall, among all seasons, missing of either observed or predictor years can significantly reduce the validation correlations and overall PCR model performance, causing larger validation correlation differences when the various calibration/validation periods are compared. Nevertheless, it is important to keep in mind that each station reconstruction has a unique relationship with the mid-latitude predictors; this reasoning might not be the only explanation that influences the validation correlation performance, since for example, one significant predictor (Orcadas) can produce more reliable station pressure reconstructions along the Antarctic Peninsula.

4.4 Chapter Summary

This chapter addressed the first research question; if the station-based Antarctic pressure reconstruction is reliable or not. This procedure was tested by performing two independent validation tests on 18 Antarctic stations, where calibration correlation, 107 validation correlation, RE, and CE were calculated and examined using two different predictor networks.

The overall results determined that the majority of the Antarctic station-based pressure reconstructions are better than climatology, with positive values of 0.2 or greater in both the RE and CE. Among all seasons, austral summer (DJF) pressure reconstructions have the best results. Results presented in this chapter found that pressure reconstructions of the Antarctic Peninsula stations are better in general, probably due to the fact that these stations are closer to predictor stations in South America, and especially Orcadas, the only predictor station south of 60°.

By examining the pressure reconstructions individually, both good and marginal results have been selected and discussed in detail. It was found that stations Faraday and

Bellingshausen have the best reconstruction skills. Interestingly, these Antarctic

Peninsula stations share a common factor in that all are strongly influenced by Orcadas.

In contrast, Byrd and Syowa are the stations that have less successful reconstruction skills in all seasons, while Casey also has relatively weak reconstruction skill in austral autumn.

The validation correlation differences in two validation tests were also presented in Table 4.5 to examine the possibility of time-sensitive effects on the PCR model. From these comparisons, it is noted that missing data in either the observations or predictor networks significantly reduce the validation correlations, rather than a model sensitivity to the calibration/validation period. Furthermore, outliers in the observations seem to also have a great impact on weakening the validation correlations. 108

In summary, with careful measurement in this chapter, it appears that the performance of the PCR models is still able to reconstruct the Antarctic station pressure fairly well, especially when their limitations are considered. Hence, the reconstructed record is considered to be a useful and reliable source, particularly for austral summer and winter, and across the Antarctic Peninsula in all seasons.

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Table 4.5: Validation correlation differences for a) summer, b) autumn, c) winter, and d) spring in the full period tests and the validation sensitivity tests (early and late periods) among each season (yellow 0.2-0.3 and red > 0.3). The suspected reasoning for larger validation correlation differences (yellow and red) is provided in the table. (a) Validation Correlation Differences - DJF p<0.5 p<0.10 Full - Full - Early - Full - Full - Early - Stations Early Late Late Early Late Late Reasoning

Amundsen-Scott 0.00 0.11 0.11 0.02 0.14 0.16

Bellingshausen 0.05 0.05 0.01 0.05 0.05 0.01

Byrd 0.04 0.25 0.21 0.04 0.20 0.16 10 observed years are missing in the full period tests Casey 0.14 0.12 0.26 0.14 0.12 0.26 3 observed years are missing in the validation sensitivity tests (early) Davis 0.13 0.19 0.32 0.13 0.25 0.38 5 observed years are missing in the validation sensitivity tests (early) Calibration during the early period is more variable (i.e., a strong increase from 1963 to 1964 Dumont d’Urville 0.12 0.11 0.23 0.19 0.12 0.31 which might impact the result) Esperanza 0.03 0.07 0.10 0.02 0.08 0.10

Faraday 0.05 0.04 0.09 0.01 0.08 0.09 Exceptionally good performance in the validation sensitivity tests (late), the validation Halley 0.08 0.14 0.22 0.04 0.18 0.22 correlation in the validation sensitivity tests is high, with values larger than 0.67 14 (13) observed years are missing in the full (early) validation tests; The validation sensitivity Marambio 0.06 0.32 0.26 0.14 0.39 0.25 tests (late) have higher validation correlations Marsh/O’Higgins 0.16 0.01 0.18 0.14 0.07 0.07 Validation sensitivity tests (late period) reproduce better reconstructions for the early 25-year Mawson 0.02 0.15 0.14 0.06 0.17 0.23 period (1957-1981); Observed pressure data is decreasing through time McMurdo/Scott base 0.01 0.16 0.16 0.00 0.16 0.16

Mirny 0.18 0.22 0.04 0.09 0.06 0.02 Validation correlation is particularly high in late period (0.797) Novolazarevskaya 0.18 0.20 0.37 0.18 0.20 0.37 4 observed years are missing in the full period and validation sensitivity tests (early) Rothera 0.10 0.07 0.02 0.11 0.01 0.11

Syowa 0.07 0.11 0.04 0.03 0.20 0.23 4 observed years are missing in the full period and the validation sensitivity tests (early);

Vostok 0.07 0.10 0.03 0.10 0.22 0.12 3 observed years are missing in the full period and the validation sensitivity tests 110

(b) Validation Correlation Differences – MAM p<0.5 p<0.10 Full - Full - Early - Full - Full - Early - Stations Early Late Late Early Late Late Reasoning

Amundsen-Scott 0.04 0.02 0.03 0.07 0.11 0.05

Bellingshausen 0.02 0.05 0.02 0.02 0.02 0.04

Byrd 0.07 0.20 0.13 0.07 0.26 0.19 11 observed years are missing in the full period tests Validation sensitivity tests (late) have a poor validation correlation (0.111 in the p<0.10 predictor network), which is perhaps caused by 7/30 missing predictor Casey 0.02 0.05 0.07 0.19 0.30 0.12 years Davis 0.11 0.12 0.01 0.06 0.12 0.06

Dumont d’Urville 0.04 0.09 0.13 0.08 0.09 0.17

Esperanza 0.05 0.04 0.09 0.03 0.03 0.00

Faraday 0.05 0.08 0.13 0.05 0.07 0.13

Halley 0.08 0.02 0.10 0.14 0.00 0.14

Marambio 0.00 0.27 0.27 0.00 0.27 0.27 14 observed years are missing in the full period tests Marsh /O’Higgins 0.16 0.09 0.08 0.09 0.06 0.03

Mawson 0.08 0.00 0.08 0.02 0.02 0.03 Station pressure has more variation in the early period (1957-1986), which McMurdo/Scott base 0.04 0.15 0.11 0.05 0.28 0.22 perhaps lowers the reconstruction skill Validation sensitivity tests (late) are weak; The station pressure strongly Mirny 0.16 0.17 0.34 0.20 0.05 0.25 decreased after 1994; Missing at least 5 predictor years in the late periods Novolazarevskaya 0.02 0.07 0.05 0.03 0.04 0.01 3 observed years are missing in the full period and validation sensitivity tests Rothera 0.14 0.12 0.25 0.08 0.17 0.25 (early) Syowa 0.14 0.07 0.08 0.13 0.04 0.10

Vostok 0.06 0.17 0.11 0.08 0.18 0.10

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(c) Validation Correlation Differences – JJA p<0.5 p<0.10 Full - Full - Early - Full - Full - Early - Stations Early Late Late Early Late Late Reasoning

Amundsen-Scott 0.11 0.09 0.20 0.17 0.00 0.17 4 predictors years are missing in the validation sensitivity tests (late)

Bellingshausen 0.01 0.02 0.01 0.01 0.02 0.01

Byrd 0.20 0.27 0.07 0.15 0.25 0.10 14 observed years are missing in the first validation tests

Casey 0.02 0.08 0.10 0.00 0.13 0.13

Davis 0.04 0.17 0.21 0.07 0.15 0.22 4 observed years are missing in the validation sensitivity tests (early)

Dumont d’Urville 0.02 0.06 0.04 0.11 0.02 0.13

Esperanza 0.05 0.02 0.03 0.07 0.04 0.11

Faraday 0.02 0.04 0.01 0.04 0.05 0.01

Halley 0.27 0.06 0.33 0.27 0.06 0.33 Outlier in 1964 weakens the performance of validation sensitivity tests (early)

Marambio 0.03 0.14 0.11 0.02 0.17 0.15

Marsh/O’Higgins 0.13 0.00 0.13 0.13 0.00 0.13

Mawson 0.06 0.01 0.05 0.03 0.08 0.10

McMurdo/Scott base 0.07 0.16 0.09 0.11 0.18 0.07

Mirny 0.03 0.05 0.02 0.04 0.14 0.11

Novolazarevskaya 0.05 0.22 0.27 0.01 0.19 0.20 4 observed years are missing in the full period and validation sensitivity tests (early)

Rothera 0.08 0.05 0.13 0.08 0.05 0.13

Syowa 0.00 0.01 0.01 0.00 0.12 0.11

Vostok 0.04 0.06 0.02 0.04 0.07 0.03

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(d) Validation Correlation Differences – SON p<0.5 p<0.10 Full - Full - Early - Full - Full - Early - Stations Early Late Late Early Late Late Reasoning

Amundsen-Scott 0.03 0.11 0.08 0.01 0.23 0.23 Validation sensitivity tests (late) are weak; Station pressure increased after 1986 Bellingshausen 0.12 0.18 0.06 0.12 0.18 0.06

Byrd 0.17 0.12 0.05 0.20 0.05 0.15 16 observed years are missing in the full period tests Casey 0.11 0.18 0.06 0.03 0.06 0.03 8 predictors years are missing in the validation sensitivity tests (late); 4 observed years are missing in the full period and validation sensitivity tests (early); Validation Davis 0.24 0.10 0.35 0.19 0.12 0.31 correlation is much higher in the early tests Dumont d’Urville 0.04 0.23 0.19 0.02 0.18 0.17 Outlier in 1977 lowers the reconstruction skills Esperanza 0.10 0.09 0.00 0.09 0.01 0.07

Faraday 0.08 0.13 0.05 0.08 0.13 0.05

Halley 0.14 0.13 0.02 0.14 0.13 0.02

Marambio 0.27 0.12 0.15 0.19 0.04 0.23 14(13) missing years in the full period tests and validation sensitivity tests (early) Marsh/O’Higgins 0.27 0.31 0.04 0.16 0.26 0.10 8 observed years are missing in the first full period tests Late periods are much weaker; Observed record shows an increasing pressure trend Mawson 0.18 0.14 0.32 0.12 0.08 0.20 during the late period McMurdo/Scott base 0.11 0.03 0.08 0.01 0.01 0.02

Mirny 0.14 0.02 0.16 0.15 0.05 0.09

Novolazarevskaya 0.20 0.18 0.38 0.22 0.15 0.37 Validation sensitivity tests (late) are much weaker, with 8 predictors years missing Rothera 0.16 0.19 0.03 0.16 0.19 0.03 5 observed years are missing in the validation sensitivity tests (early), yet they have a Syowa 0.15 0.08 0.23 0.18 0.06 0.24 much higher validation correlation 3 observed years are missing in the full period tests; Outliers in 1977 and 1996 Vostok 0.12 0.04 0.16 0.20 0.00 0.20 weaken the reconstruction performance 113

CHAPTER 5: SIGNIFICANT EVENTS IN 1905-2011

This chapter examines the uniqueness of changes in the observational data by comparing it to the reconstructed station pressure variability and trends during 1905-

2011. As the overall reconstruction reliability has been demonstrated in Chapter 4, the potential roles of forced (changes unique in the full record, perhaps due to ozone depletion and/or greenhouse gases increases) and natural variability (changes observed previously in the reconstruction) can be interpreted. This chapter first examines the temporal variability in the station pressure reconstructions in order to foster a better understanding of the uniqueness trends in the historical record; specifically, substantial deviations (i.e., peaks and troughs) will be identified in the pre-observation period, as these features may possibly portray the significant events before 1957 when the instrumental records were not yet started. Then, station pressure trends are investigated by using two different approaches, such as studying the trends by separating 1905-2011 into two periods, 1905-1956 and 1957-2011, with special interest on the latter period when the greenhouse gases and ozone forcing became more significant. In this chapter, graphical/visual examples will be given if the similar features appear in majority of the stations for clarification.

5.1 Low-frequency variability from the reconstructed records

Antarctic station pressure can highly fluctuate interannually. These variations can sometimes make it difficult to identify trends or changes distinctly. Thus, a data smoothing technique has been introduced to eliminate the interannual noise and extract persistent low-frequency trends and patterns throughout a longer time period. In this 114 research, decadal mean pressure comparison and 7-year/11-year smoothed low-pass triangular filters are used. The decadal mean pressure comparison simply calculates the station pressure mean for each decade from the reconstruction records, whereas the smoothed data are calculated by a weighted sum of the neighboring years.

5.1.1 Decadal comparison

Since the reconstructed record starts in 1905 and ends in 2011, the decadal mean will not be complete and as meaningful in the 1900s and 2010s. Hence, decadal variability of station pressure will mainly focus on the 1910-2000 period. To investigate station pressure change more precisely, stations have been separated into East and West

Antarctica based on their geographical locations. In West Antarctica, this division focuses primarily on the Antarctic Peninsula variations, since most of the stations are located in this region.

In general, a good agreement can be found among stations in DJF, where the

1960s-1970s are the decades that have the generally displayed highest decadal station pressure in West Antarctica and the period from 1990-2000s experienced the lowest decadal station pressure in most of the East Antarctic stations in austral summer. For

West Antarctica, seven out of eight stations (except Marsh/O‟Higgins) have shown the highest decadal mean station pressure in about the 1960s-1970s (an example is shown in

Fig. 5.1a), where the rest of the decades appear to have similar decadal mean pressure through time in DJF. Examining the East Antarctic station pressure reveals a different scenario, where the lowest decadal mean pressures occurred in all East Antarctic stations during the 1990s-2000s (an example is shown in Fig. 5.1b); the lower decadal mean 115 pressure at this time is also seen in the West Antarctic stations of Halley and Byrd. This key feature would not have been as known to be as important without the reconstruction data prior to the 1960s. At Amundsen Scott, the same situation can be also seen. Hence, these strong agreements across the East Antarctic stations indicate that some environmental forcing might play an important role in influencing East Antarctica in recent decades. Notably, these findings also indicate the East Antarctic station pressure has been decreasing rapidly since the 1980s, most likely tied to the influence of ozone that is noted in austral summer in particular (Thompson and Solomon 2002; Miller et al.

2006; Fogt et al. 2009).

Figure 5.1: Decadal comparison of Esperanza (a; West Antarctica) and Mawson (b; East Antarctica) in DJF as the examples used to show most of the distinctive features.

During MAM, in general, a good decadal surface pressure agreement is found in both East and West Antarctic stations. In West Antarctica, nearly all stations (with exceptions in Halley and Byrd) have seen their lowest decadal station pressure in the

1960s (an example is shown in Fig. 5.2a), although at Byrd the 2000s appeared even 116 lower. In contrast, the 1980-2010 periods appear to have the highest decadal station pressure in austral autumn (5/8; an example is given in Fig. 5.2a), although only slightly higher than the others. In East Antarctica a different scenario can be seen, where the highest decadal station pressures took place in the 1950s (an example is given in Fig.

5.2b), with 7/9 stations that support this result. However, there is less agreement in finding the lowest decadal surface pressure, where only 5/9 stations support that the

1930s result is the lowest (Fig. 5.2b). Results from Amundsen Scott agree well with the finding in East Antarctica that the highest decadal station pressure occurred in the 1950s and the lowest one occurred in the 1930s. Hence, it seems that East and West Antarctic station pressures behave differently in MAM through time; this finding is consistent with the different trends in geopotential height across West Antarctica and East Antarctica observed by Neff et al. (2008).

Figure 5.2: Decadal comparison of Faraday (a; West Antarctica) and Dumont d‟Urville (b; East Antarctica) in MAM as the examples used to show most of the distinctive features.

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In JJA, there are no significant decadal events that match with all Antarctic stations. Nevertheless, interesting events can still be observed when examining East and

West Antarctica separately. In West Antarctica, the lowest decadal station pressure occurred during the 1990s (an example is shown in Fig.5.3a), although this is only true in the Antarctic Peninsula stations. In addition, about half of the West Antarctic stations reveal that the highest decadal pressure occurred in the 2000s, with the 1950s results looking to be similar, yet slightly weaker, in magnitude (Fig. 5.3a). In East Antarctica, a general stronger agreement can be found among the stations. In particular, 6/9 stations present the lowest decadal station pressure as having occurred in the 1930s, whereas the highest decadal station pressure existed in the 1960s-1970s (7/9; an example is shown in

Fig. 5.3b). Interestingly, even though Halley is a West Antarctic station, it agrees well with the finding in East Antarctic stations, perhaps reflecting more of an Antarctic continent versus Antarctic Peninsula difference rather than a simple hemispheric difference. Furthermore, Amundsen Scott also reveals the same lowest decadal station pressure in the 1930s, yet its highest decadal station pressure took place in the 1960-

1970s. Hence, examining decadal station pressure in austral winter reveals that East and

West Antarctica also behave differently through time. 118

Figure 5.3: Decadal comparison of Bellingshausen (a; West Antarctica) and Casey (b; East Antarctica) in MAM as the examples used to show most of the distinctive features.

During austral spring (SON), in general, more than half of the stations show that the lowest decadal station pressure occurred in the 1930s. Specifically, in West

Antarctica, the strongest agreement is that the 1930s and 1960s appear to have lower decadal station pressures (about 5/8), whereas the highest decadal station pressure occurred in the 2000s (7/8; an example is shown in Fig. 5.4a). A different story can be found in East Antarctica, where 6/9 stations support that the lowest decadal station pressure occurred in the 1930s (an example is shown in Fig. 5.4b). In terms of high decadal mean station pressure, it appears to be higher from the 1950s-1970s, although only about half of the stations support these generalized conclusions. Hence, a good agreement can be found among most of the Antarctic stations, with the lowest decadal mean station pressure found in the 1930s. 119

Figure 5.4: Decadal comparison of Bellingshausen (a; West Antarctica) and Mawson (b; East Antarctica) in SON as the examples used to show most of the distinctive features.

To summarize the decadal variations, it is clear that station pressures in East and

West Antarctica tend to behave differently through the seasons. In DJF, a decreasing decadal station pressure can be well-depicted from the 1990s to the 2000s among East

Antarctic stations in DJF. Interestingly, among MAM, JJA, and SON, a consistent report of the lowest decadal mean pressures occurred in East Antarctica during the 1930s, where these events can also be seen in West Antarctica during the austral spring. Further, across all seasons, the majority of the highest and lowest decadal mean pressures occur during the observation period (during or after the 1950s), again highlighting the reduced variability from earlier records now afforded by the reconstructions.

5.1.2 7-year/ 11-year smoothed low-pass triangular filters

The 7-year/11-year data smoothing technique is another method that has been investigated to eliminate the interannual noise and extract persistent low-frequency trends and patterns throughout the reconstructed record. In the 7-year smoothed filter, the consecutive years (7 years in this case) are given numbers as follows, (1, 2, 3, 4, 3, 2, and 120

1). The sum of these numbers is 16, so the weights are simply these numbers divided by the sum, centered on the middle year of the 7-year period. Multiplying the weights to the data and summing across the seven years produces the value for each time step. For instance, when a smoothed value for 1908 is calculated, the year 1908 itself is weighted a quarter (4/16); the neighboring years 1907 and 1909 are weighted in 3/16; 1906 and 1910 are weighted in 2/16, and so forth. In this technique, peaks and troughs are picked only if both the 7-year and 11-year smoothed filters show similar results.

As examination of the individual pressure reconstructions for each station reveals that during DJF stations have shown the best agreement in having the same peaks or troughs, though other seasons also do fairly well. In DJF, three main troughs are found, where two of these that occur prior to ozone depletion and greenhouse gases forcing being much weaker (the best representation is shown in Fig. 5.5). The deepest troughs can be found in all stations during 1961 (Fig. 5.5), with a significantly strong decrease of

~4 hPa in 5-year time step that can be seen in Casey and Mawson. These troughs match perfectly with the SAM indices in the Fogt et al. (2009) study, as the positive SAM index indicates decreasing pressure in Antarctica. The second persistent trough occurred around

1915 (Fig. 5.5), with 16/18 stations supporting this finding; the troughs are best shown in the 7-year smoothed data. In the second half of the twentieth century, the most unique events can be found among the stations, where many station pressures have been decreasing since 1960s (an example is shown in Fig. 5.5), although the Antarctic

Peninsula stations show fairly weak/almost no decrease in station pressure. The decreasing pressure across East Antarctica in recent decades, as suggested earlier, is most 121 likely due to stratospheric ozone depletion. During this period, the key observation is that the troughs are exhibited around 1997, the lowest station pressure in the majority of stations during the 1905-2011 period (Fig. 5.5). Interestingly, the 7-year smoothed data in

2007 shows that the data again decreases to the same level as in 1997 (some stations even have values lower than in 1997), which again correlates well with the strong positive

SAM (Marshall 2003). Moreover, during the austral summer, the highest peak exists during the year 1965, where a majority of stations show that their station pressures are the highest in the full period of 1905-2011 (Fig. 5.1). This finding correlates well with the minima in the SAM index during the same period of time. Thus, the troughs and ridges that are found in DJF among the stations, particularly in East Antarctic stations, have a good agreement with the Marshall (2003) observed and Fogt et al. (2009) reconstructed

SAM indices.

Figure 5.5: 7-yr (blue) and 11-yr (red) smoothed data of Novolazarevskaya in austral summer (DJF) as an example of most of the distinctive features. The observed 7-yr (11- yr) is shown in green (purple), with the confidence intervals shaded in black (blue). 122

During MAM, the station pressure records in general seem to display repetitive pressure variability, now made more apparent by the longer record from the reconstructions. The minima are weaker compared with those from the austral summer, yet again three distinctive minima were still depicted among the majority of stations. The first local minimum occurred in the 1930s (the best representation is shown in Fig. 5.6), with about 14/18 stations showing a similar minimum at this time and about 5/14 stations having nearly their lowest station pressure value in the full period. A similar story exists around 1997, where a significant drop resulted in station pressure values close to those of the 1930s (Fig. 5.6), with about 13 stations that support the finding. It is perhaps unsurprising that the peaks are seen in the Fogt et al. (2009) SAM index reconstruction at the same period of time. Furthermore, a weaker (yet still pronounced compared to the neighboring years) minimum exists around 1962 (Fig. 5.6); this minimum appears in about 11 of the stations investigated in this study.

123

Figure 5.6: 7-yr (blue) and 11-yr (red) smoothed data of Dumont d‟Urville in austral autumn (MAM) as an example of most of the distinctive features. The observed 7-yr (11- yr) is shown in green (purple), with the confidence intervals shaded in black (blue).

In JJA, the smoothed data again show repetitive pressure variability throughout the years. During the austral winter, the most pronounced minimum can be seen around

1937, where a representative station is shown in Fig. 5.7. This is the time when more than half of the reconstructions show significant low pressure anomalies during the period of 1905-2011. Moreover, most of the reconstructions agree that stronger peaks exist in the years 1914 and 1962. Nevertheless, among all the reconstructions in austral winter, although other minima (1922 and 2000) and maxima (1928 and 1946) do exist in the full period, they seem to be smaller and less pronounced. Furthermore, the occurrence of the extremes also have a strong correlation with the SAM indices (Fogt et al. 2009), yet these are not as strong as austral summer in magnitude. 124

Figure 5.7: 7-yr (blue) and 11-yr (red) smoothed data of Davis in austral winter (JJA) as an example of most of the distinctive features. The observed 7-yr (11-yr) is shown in green (purple), with the confidence intervals shaded in black (blue).

In SON, a persistent minimum can be seen in all reconstructions in the 1930s (the most representative station is given in Fig. 5.8), where the minima tend to be large and deep, indicating consistent decreasing low pressure anomalies that lasted longer than a decade. Then, a strong peak followed right after in 1941 and again in 1970 in all stations

(Fig. 5.8), with the majority of the stations showing that the peaks were slightly higher in

1941. Furthermore, weaker local minima and maxima are also embedded in the reconstructions, such as peaks in 1915 and 1925 and a dip in 1998, yet they appear to be small and slightly fewer stations support them. These interannual fluctuations once again correlate well with the Fogt et al. (2009) reconstructed SAM indices.

125

Figure 5.8: 7-yr (blue) and 11-yr (red) smoothed data of Casey in austral winter (SON) as an example of most of the distinctive features. The observed 7-yr (11-yr) is shown in green (purple), with the confidence intervals shaded in black (blue).

In summary, the persistent troughs and peaks in all reconstructions are clearly depicted. The most noticeable and interesting finding exists in DJF, where the majority of stations, particularly East Antarctic stations, have shown a strong decreasing station pressure in the second half of the twentieth century; these unique trends are likely tied to ozone depletion, and will be discussed in the following section. Notably, in MAM, JJA, and SON, strong troughs are also found in most of the Antarctic stations during the

1930s, indicating there might be some external forcing influencing the Antarctic station pressures. Among all the troughs and peaks, it appears that they have a strong connection with the SAM index, which further enhances the certainty of the reconstruction skills, as the SAM and station-based reconstructions were conducted independently. 126

5.2 Historical station pressure variability and trends

Here, two ways are employed to investigate linear changes at Antarctic stations using the long-term data garnered from the reconstructions. First, trends are examined separately during 1905-1956 and 1957-2011(hereafter referred to as the „former period‟ and „latter period‟). Second, linear trends are examined using 30-/40-/50-year running trends to highlight temporal changes in the trend sign/magnitude. The first method determines whether the trend during the first half of the 20th century differs from that of the second half. For the second approach, a 30-year period is chosen as the minimum period in which to calculate a trend to both remove the influence of strong outliers as well as to include enough years in the sample so as to capture a reliable estimate of the internal (natural) interannual climate variability. The 40-year and 50-year trends were also calculated to identify consistent results.

5.2.1 Two period trends comparison

Figures. 5.9a-d demonstrates the former period reconstruction trends (1905-1956) and latter period reconstruction trends (1957-2011) in all stations, with the 95% confidence intervals provided. Notice that the error bar intervals represent the confidence levels, where the smaller intervals have more certainty of trend magnitude (i.e., in these cases, the linear trend describes more of the variation of the actual data). It is also important to keep in mind that the error bars are based on the t-distribution, meaning the

“center value” of the error bar has the highest probability and is the most likely estimate of the true (population) trend. 127

In DJF, an interesting phenomenon is quickly evident, in which nearly all stations capture a more negative trend when comparing the former period to the latter period (Fig.

5.9a), though some trends are not significantly different from zero (and therefore, may not have a trend with p<0.05) as the 95% confident intervals cross the zero line. In the former period, three stations present significant positive trends, namely Amundsen Scott

(0.456±0.378 hPa decade-1), Mirny (0.433±0.385 hPa decade-1), and Novolazarevskaya

(0.435±0.429 hPa decade-1). In terms of the latter period (1956-2011), more negative station pressure trends can be easily depicted among all the stations with much stronger consistency; 12/18 stations have a confidence interval range that falls into negative values. In particular, Novolazarevskaya and Mawson have shown strong negative trends among all stations during the latter period, with station pressure trends of -0.999±0.456 hPa decade-1 and -0.776±0.433 hPa decade-1, respectively.

Interestingly, the 95% confidence intervals of the former and latter period station pressure trends in the East Antarctic stations (except Davis) and Amundsen Scott do not overlap, where Novolzarevskaya and Amundsen Scott have the largest “center value” difference in two periods, with 1.434 hPa decade-1 and 1.207 hPa decade-1, respectively.

At these stations, there has been a statistically significant (p<0.05) change in the trends between the former and latter periods. Hence, ozone depletion and greenhouse gases might be highly influencing the East Antarctic stations through time, as noted in trends of the SAM index (Fogt et al. 2009 and references therein) and in previous discussions herein. Nevertheless, when compared with observed trend, only three stations in East 128

Antarctica and one station in West Antarctica have the same sign of significant trends, as shown in Chapter 4 (Fig. 4.9a).

During MAM, a similar (and yet larger) decreasing set of trends in the latter period is seen in most of the Antarctic stations (Fig. 5.9b). In East Antarctica, most stations appear to have strong significant negative trends in the latter period. In West

Antarctic, stations also appear to have positive trends in both periods, although most pressure trends there are not significant, as it slightly pass the zero line. Nevertheless, most of the observed trends have shown similar results, yet they are not statistically significant, as the trends cross the zero (Fig. 4.9b). During the former period, there are a total of three stations that display statistically significant (p<0.05) pressure increases

(Syowa 0.352±0.268 decade-1; Mirny 0.701±0.423 decade-1; and Casey 0.428±0.337 decade-1). In contrast, during the latter period, significant negative trends are found in all

East Antarctic stations and Amundsen Scott. In particular, Mirny, Casey and Amundsen

Scott have the most significant negative trends, with station pressure trends of -

0.996±0.296 hPa decade-1, -0.867±0.29 hPa decade-1, and -0.736±0.322 hPa decade-1.

Notably, eight stations have statistically significant (p<0.05) trend changes in magnitude, as the confidence intervals about their surface pressure trends between the two periods do not overlap. Particularly, Mirny and Casey show the strongest trend difference (“center value”) in two periods, with value different of 1.697 hPa decade-1 and 1.295 hPa decade-1, indicating that perhaps more high surface pressure events occurred in the first half of twentieth century, while low surface pressure events occurred in the second half. 129

Figure 5.9: Reconstruction trends in 1905-1956 (black) and 1957-2011 (DJF 1957-2010; red) comparison for a) summer, b) autumn, c) winter, and d) spring. Error bars represent 95% confidence intervals (maximum and minimum values).

130

In JJA (Fig 5.9c), most of the pressure trends are not significantly different from zero. In JJA, the exceptions are Byrd (-0.491±0.434 hPa decade-1) and Vostok

(0.608±0.529 hPa decade-1) in the former period, whereas three significant trends can also be found in the latter period (Byrd -0.491±0.434 hPa decade-1; Mirny -0.653±0.479 hPa decade-1; Amundsen Scott -0.417±0.393 hPa decade-1). Furthermore, during the latter period, Byrd has a significant positive observed trend, which is contradictive to the significant negative reconstructed trend. This perhaps relates to a relatively weak pressure reconstruction skill in Byrd (as mentioned in Chapter 4) in this season.

In SON (Fig. 5.9d), an interesting feature can be found, where all the Antarctic

Peninsula stations display statistically significant (p<0.05) positive trends in the latter period. In contrast, four stations have an opposite trend in the same period, which are

Byrd, Mirny, McMurdo/Scott base, and Amundsen Scott. The rest of the station trends are not significant as they are close to zero in both seasons. As a reminder, the observed trends are not statistically significant at p<0.05. This perhaps is not surprising as it has been discussed in Chapter 4, where the reconstruction skill is generally lower in austral spring.

Overall, examining the former and latter periods has uncovered several interesting pressure trend differences among the seasons. In DJF and MAM, most of the East

Antarctic stations have shown significant negative trends in the latter period.

Furthermore, these stations also appear to have statistically significant (p<0.05) trend changes in magnitude, since their surface pressure trends between the former and latter periods do not overlap. These highest numbers of significant trends are consistent with 131 trends in the SAM index (Marshall 2007; Fogt et al. 2009). It would appear that these surface pressure changes might perhaps be linked to ozone depletion and greenhouse gases, especially in DJF, as this has been the season of greatest influence on the tropospheric circulation of the Southern Hemisphere (Thompson and Solomon 2002;

Arblaster and Meehl 2006; Miller et al. 2006; Fogt et al. 2009). In SON, all the reconstructions for Antarctic Peninsula stations show a significant positive trend in the latter period, indicating that station pressure might have been increasing during the 1957-

2011 period. Nevertheless, even though the observed trends appear insignificant compared with the significant reconstructed trends, most of the stations do appear to have the same sign of trends in the latter period; the reconstructions thus point to a unique change in the trends at these stations when comparing the former and latter periods.

5.2.2 30-/40-/50-year trends

The second method used to examine the station pressure trends is the 30-/40-/50- year running trend method. In DJF, a shift to a more negative trend can be seen in a majority of stations after 1960, except Marsh/ O‟Higgins and Faraday (the best examples are shown in Figs 5.10a and b). Among these stations, there are 10/18 stations that started a negative trend even prior to 1960, in the early 1940s. These running trends clearly highlight the historical importance of the recent DJF trends, where no other 30-year in the entire period shows negative trends equivalent or stronger (i.e., more negative) in magnitude. Specifically, in East Antarctica, the decreasing trend magnitude is more pronounced and these trends remain strongly negative (-0.1 year-1 or lower) since the

1960s (Fig. 5.10b), indicating the station pressure has been decreasing strongly and 132 consistently. Although the decreasing trends tend to be weaker in West Antarctic stations, similar results can also be found, as shown in Fig. 5.10a. Furthermore, reconstructed trends in the East Antarctic stations capture the observed trends very well, whereas the observed trends in West Antarctic stations have less agreement with the reconstructed trends.

Figure 5.10: 30- (blue), 40- (red), 50- (green), and observed 30-year (purple) running trends at Faraday (a; West Antarctica) and Mawson (b; East Antarctica) in DJF as the examples used to show most of the distinctive features. The year that is shown in the figure is the first year in which the trends are calculated.

In MAM, a recent negative trend can also be seen in all stations after 1960, the best examples of which are shown in Figs. 5.11a and b. Nevertheless, there are some trends that are only confined to either East or West Antarctica, as discussed in the preceding section. In West Antarctica, negative trends can also be seen in the period of

1930-1950, where the magnitude of the trends even exceeded the recent trend at some of the stations, particularly those stations north of the Antarctic Peninsula (Fig. 5.11a). 133

Immediately following was an increasing trend that began and peaked in 1960, which was the time when the strongest positive 30-year trend occurred. Another peak also developed around the 1920s, although the magnitude of the trend was slightly weaker

(Fig 5.11a). In contrast, in East Antarctica, the highest 30-year trend was not seen in

1960. Instead, it occurred in 1930 and was much stronger than the one in West Antarctica

(Fig 5.11b). This perhaps indicates there might be another forcing or variability that was influencing West Antarctica, as the recent trends (when ozone depletion and/or greenhouse gas increases would have their greatest influence) are not unique in the reconstructions. Interestingly, nearly all East Antarctic stations have shown a shift towards weaker negative/ more neutral pressure trends since the 1970s (Fig. 5.11b), indicating station pressure in East and West Antarctica might be affected by different natural variability.

Figure 5.11: 30- (blue), 40- (red), 50- (green), and observed 30- year (purple) running trends at Esperanza (a; West Antarctica) and Mirny (b; East Antarctica) in MAM as the examples used to show most of the distinctive features. The year that is shown in the figure is the first year in which the trends are calculated. 134

During JJA, in general, 30-year trends appear to be positive from 1917-1940, whereas years after 1940 tend to be negative (the best examples are shown in Figs. 5.12a and b). In East Antarctica, the 30-year trend began to increase from 1915-1937 and reached its peak around 1937 (Fig. 5.12b). A trend magnitude reduced right after this, reaching its lowest value around 1963. Nevertheless, this shift towards more negative trend values might not be unique, as a similar negative trend is seen around 1911

(Fig.12b). A similar finding can also be found in West Antarctica, although there is less consistency among the stations, especially those that are located at the north of the

Antarctic Peninsula. Nevertheless, strong 30-year positive trends are seen in nearly all stations since 1970 (Figs.12a and b). Interestingly, in West Antarctica, Esperanza even reached its highest peak in its last 30-year trend. In fact, this 30-year trend has rapidly increased from nearly -0.16 year-1 to 0.09 year-1 from 1970-2011 (Fig. 5.12a) in over approximately 12 year time step, which has never been seen before. This finding is in agreement with the decadal mean pressure analysis in section 5.1.1 that station pressure across the Antarctic Peninsula was unusually low in the 1990s and abnormally high in the

2000s in JJA (Fig. 5.3). 135

Figure 5.12: 30- (blue), 40- (red), 50- (green), and observed 30-year (purple) running trends at Esperanza (a; West Antarctica) and Davis (b; East Antarctica) in JJA as the examples used to show the distinctive features. The year that is shown in the figure is the first year in which the trends are calculated.

During SON, in general, positive 30-year trends are seen from 1920-1935 (the best examples are shown in Figs. 5.13a and b); these trends are the strongest in the 20th century in the majority of East Antarctic stations. In West Antarctica, half of the stations have the lowest 30-year trends around 1940 (Fig. 5.13a). It was not until 1970 that a strong and persistent increasing 30-year trend is seen in nearly all stations up until the last

30 years; half of the stations even have the strongest trend in the last 30 years (Fig.

5.13a). This finding can also be depicted from the decadal mean comparison, as shown in

Fig. 5.4. Hence, this key feature indicates that some environmental forcing might play an important role that is recently influencing the entire Antarctic stations in austral spring, particularly in West Antarctica. 136

Figure 5.13: 30- (blue), 40- (red), 50- (green), and observed 30- (purple) year running trends at Esperanza (a; West Antarctica) and Davis (b; East Antarctica) in JJA as the examples used to show the distinctive features. The year that is shown in the figure is the first year in which the trends are calculated.

In summary, this section examined the station pressure trends by using two methods – two period trends comparison and 30-/40-/50-year trends. In the first method, only austral summer and autumn showed a significant different trend during 1905-1956 and 1957-2011, with the station pressure decreasing significantly in the latter period.

Examining the first method yields different results among the seasons. In austral summer and austral autumn, the majority of the stations have statistically significant (p<0.05) negative trends in the latter period (1957-2011), which are consistent with the SAM indices (Marshall 2007; Fogt et al. 2009). In contrast, the West Antarctic stations also have similar trends in the latter period, although most of them cannot be claimed as statistically significant. Additionally, in SON, all the stations in the Antarctic Peninsula 137 have a significant positive trend in the latter period, indicating some forcing might take place that influences the Antarctic Peninsula greatly.

In the second method, examining the running 30-year trends found a similar result in DJF and MAM, where the majority of the stations have shown the strongest negative trend in the entire 20th century after 1960, especially in East Antarctica. This key feature is also supported by the 40- and 50-year trends. In contrast, the 30-year trends in austral autumn (only in East Antarctica), austral winter, and austral spring appear to be increasing in magnitude since 1970, although these trends seem to repeat similar magnitudes seen previously in the reconstructions.

Hence, in light of these new unprecedented (in the reconstructed 100+ year records) historical trends, it is evident that the stratospheric ozone depletion is primarily responsible for the significant changes in DJF (perhaps in MAM as well). In regards to the increasing trends (after 1970) in MAM, JJA, and SON, internal variability may also be playing an important role across Antarctica, but further attribution work is needed to understand these changes, especially in context of the lower reconstruction skill in MAM and SON.

5.3 Chapter Summary

The purpose of this chapter was to address the second research question, namely to better understand station pressure variability garnered from the reconstructed record during 1905-2011. In order to examine this, multiple approaches were taken. They are 7-/

11-year smoothing, comparison of decadal means, and comparison of trends (using both fixed and temporally varying time periods). Since studies have found a warming trend in 138

West Antarctica along with the Antarctic Peninsula (Steig et al. 2009; O‟Donnell et al.

2011; Bromwich et al. 2013) and a slight cooling trend in East Antarctica (Turner et al.

2005; Steig et al. 2009), East and West Antarctica have been examined separately.

Examining two separate regions can help understand the 20th century Antarctic station pressure variability and change more thoroughly.

Features generally well-determined by the station data are the recent strong negative trends in DJF in East Antarctic stations. In particular, the lowest station decadal pressure can be seen in the 1990s-2000s. Furthermore, the 7-/11-year smoothed data also present a strong and persistent decreasing station pressure (mainly in East Antarctica) in the second half of the 20th century. In contrast, West Antarctic stations have shown the highest station pressure occurred around the 1960s-1970s, which are also supported by two of the low-frequency variability analyses. The historical station pressure variability and trends from the reconstruction records were also examined, in which most of the East

Antarctic stations have shown significant negative trends in the latter period. In regards to the temporal changes, 30-/40-/50-year trends also support the finding that nearly all East

Antarctic stations have the strongest negative trend after 1960. Hence, it is most likely that this unprecedented historical trend in the 100+ year reconstructed record is tied to the influence of ozone and greenhouse gases (Thompson and Solomon 2002; Arblaster and

Meehl 2006; Miller et al. 2006; Fogt et al. 2009).

In MAM, examining the low-frequency trends found that the lowest decadal mean pressure occurred in nearly all of the East Antarctic stations during the 1930s and 1990-

2010. These findings are also supported by the 7-/11- smoothed data, as the deep trough 139 is found during the same period of time. In West Antarctica, the 1960s have the lowest decadal station pressure throughout 100+ years. Since these special events only occurred in the Antarctic Peninsula, it is likely there might be other factors, such as a stronger

Amundsen-Bellingshausen Seas Low has a great influence during that time (i.e., Hosking et al. 2013). Regarding the historical station pressure variability and trends from the reconstruction records, nearly all of the East Antarctic stations have shown significant negative trends in the latter period from the two trend comparison tests. Furthermore, in the 30-year smoothed method, the strongest negative trend is also found around the

1960s. It is then immediately followed by a shift towards weaker, but still negative trends, up until the last 30 years.

In JJA, in general, the decadal mean pressure and 7/11 year smoothed data both indicate that the 1930s have the lowest pressure, while the 1960-1980 period has the highest pressure in the East Antarctica. In West Antarctica, stations appear to have the lowest decadal mean station pressure in the 1990s, although this is relatively weak and less significant than in DJF and MAM. In terms of the historical station pressure variability, most of the stations have shown the trends are insignificant in the former and latter period. Nevertheless, in East Antarctica, the 30-/40-/50- smoothed tests have shown a decreasing trend around the 1910s and an increasing trend around 1935 which are consistent with the occurrences of local maxima and minima when investigating the low- frequency variability. In West Antarctica, stations have shown a rapidly increasing trend in Antarctic stations since 1970. Although the lowest decadal station pressure (1990s) in

JJA appears to exist in West Antarctica, the ozone forcing might not be able to provide 140 good reasoning in this case, since the trend is not as significant as DJF and MAM, and outside of the seasons of studied ozone influence on the troposphere (Thompson and

Solomon 2002). Hence, the 1990s is a low station pressure decade that might be influenced largely by alternative natural forcing.

In SON, the low-frequency variability analyses have found that the 1930s was the lowest station pressure decade in the Antarctic stations, whereas the highest decadal mean occurred in the 2000s in West Antarctica, which again is supported by both the decadal mean test and 7-/11- smoothed method. In regards to the historical station pressure variability, the Antarctic Peninsula stations display statistically significant positive trends in the latter period. In fact, the 30-/40-/50- smoothed tests also have shown an increasing trend in Antarctic stations since 1970, which is similar to the austral winter. However, the mechanism that induces the increasing trend is still unclear.

Interestingly, among SON (including West Antarctic stations), MAM and JJA, a consistent report of the lowest decadal mean pressures occurred in East Antarctica during the 1930s.

In this chapter, the assessment of the station pressure variability shows statistically significant (p<0.05) and unique pressure decreases in DJF and MAM, particularly in East Antarctic stations, during the last few decades. Based on previous attribution analyses (Thompson and Solomon 2002; Arblaster and Meehl 2006; Miller et al. 2006; Fogt et al. 2009), these trends are most strongly linked to stratospheric ozone depletion. It is yet unclear of the driving mechanism of the continuous shift in trend magnitude towards more positive/weakly negative values can clearly be seen in austral 141 autumn (only in East Antarctica), austral winter, and austral spring since 1970, which also appear unique in the 100+ year reconstructed records at most stations, although these trends are not as statistically significant as austral summer and autumn. Particularly in

West Antarctic stations, the increasing trend is unprecedented in JJA and SON. While the mechanism of this increasing trend is also unclear, further investigation may lead to an explanation in the future.

142

CHAPTER 6: SUMMARY AND CONCLUSIONS

The main purpose of this research is to reconstruct Antarctic station pressure records during the period 1905-2011. The reconstructions were based on Principal

Component Regression (PCR), using significantly correlated (p<0.05 and p<0.10) long- term Southern Hemisphere pressure observations as the predictors. The first question of this thesis is to examine the accuracy of the PCR model in producing reliable pressure reconstructions. Of particular interest was determining the most optimal set of predictor data and calibration procedures to obtain the most reliable reconstruction. Several independent validation techniques were measured and examined explicitly in Chapter 4.

Using these station reconstructions, the second thesis question was addressed in Chapter

5. The uniqueness of the ongoing Antarctic circulation changes was examined and analyzed using both the reconstructions and observation records. In particular this thesis was interested in determining if any of the circulation changes were unique during the last 100+ years, and thus likely tied to external forcing such as stratospheric ozone depletion and increasing greenhouse gas concentrations.

Four datasets were used in this thesis. Overall Antarctic station pressure records were collected from the Reference Antarctic Data for Environmental Research Archive

(READER), whereas the Southern Hemisphere mid-latitude stations were collected from

Global Historical Climatology Network (GHCN), dataset ds.570.0, and the Climate

Research Unit (CRU). In order to examine the PCR model for the 18 total Antarctic station pressure reconstructions, two assessment tests were done in Chapter 4 to answer the first thesis question: a leave-one-out cross validation test and validation/calibration 143 temporal sensitivity tests. The former test used the full period of 1957-2011 for the model calibration, whereas the latter tests separate the calibration period into the first 30 years

(1957-1986) and last 30 years (1982-2011), with the remaining 25 years in each calibration serving as the validation period. In each approach, calibration correlation, validation correlation, reduction of error (RE), and coefficient of efficiency (CE) were calculated for both the p<0.05 and p<0.10 predictor networks as a means of assessing reconstruction skill and accuracy.

To address the first thesis question, several tests have been illustrated to examine the Antarctic station pressure reconstructions in great detail. It is believed that the PCR models are able to reconstruct the Antarctic station pressures as far back as to 1905, or at minimum they are better than the existing record. Hence, the station pressure reconstructions are considered to be a more reliable and valuable source of data.

In regards to the calibration correlations, it is clear that austral summer has the best performance, in which nearly all Antarctic stations had calibration correlations larger than 0.6. In austral winter, the majority of the stations also have similar results, as most of the stations have calibration correlations higher than 0.6. In contrast, during austral autumn and spring, the overall calibration correlations are slightly weaker. Nevertheless, all stations still have positive calibration correlations. In particular, Antarctic Peninsula stations generally have higher values across all seasons, as the nearby predictor station

Orcadas (60.7°S, 40.7°W) is highly correlated with these stations.

In terms of validation correlations, a similar result was seen among all seasons, where austral summer again has the highest number of stations (78% or above) that have 144 r>0.6. Specifically, the late period reconstruction does exceptionally well, with a minimum of 12/18 stations with validation correlations of r>0.8 in both predictor networks, meaning the PCR model is very effective at capturing early period (1957-1986)

Antarctic pressure variability in austral summer. In austral winter, the results are slightly weaker than in austral summer, yet more than 10 stations still have r>0.6 for all reconstruction periods. In austral autumn and spring, the number of stations that have r>0.6 is lower than for other seasons; nonetheless, overall, nearly all stations in both predictor networks have validation correlations of 0.4 or higher, where stations in the

Antarctic Peninsula appear to have higher values.

Assessment of RE and CE was also performed, as these are two rigorous statistical techniques used to examine the accuracy of the PCR models, in which a positive value of RE and CE indicate the PCR models are better than using the climatological mean. Overall, in each season, the majority of the reconstructions have values larger than 0.2 for both RE and CE, where austral summer has the highest number of stations with RE/CE larger than 0.4. Furthermore, Antarctic Peninsula stations again appear to have higher values of RE and CE in each season.

To further examine the station pressure reconstructions, a comparison of the observed trends and the reconstruction trends (1957-2011) were conducted, as the similar trends ensure a high reconstruction skill. Hence, the linear trends between the observed and best reconstruction records (either p<0.05 or p<0.10 predictor networks) were examined. In general, the reconstruction trends capture the observed trends very well, particularly in austral summer and winter. Austral autumn and spring display a lower 145 ability of the PCR model to reproduce the observed trends, even though the observed trends are not statistically different.

Individual-based station pressure reconstructions were also analyzed, where stations that have the highest or lowest reconstruction skills were selected. Faraday and

Bellingshausen (Antarctic Peninsula stations) have the highest reconstruction skills, in which Orcadas plays the most significant role, as it is the predictor that has the strongest correlation with the Antarctic Peninsula stations. In terms of the low skill pressure reconstructions, Byrd and Syowa have notably lower reconstruction skills, mainly due to missing years in the predictand and/or predictor network. In particular, only 76% (91%) of data is complete in Byrd (Syowa) in the full period through 2011. Furthermore, the geographic locations perhaps also had a large influence on the PCR models, as the

Southern Hemisphere mid-latitude stations (predictors) are greatly distanced from Byrd and Syowa, and less strong teleconnections exist to the continental regions of the

Southern Hemisphere. Nevertheless, even at these stations that display the lowest skill overall, the reconstructed records are still able to reproduce Antarctic station pressure variability better than using the climatological mean alone (i.e., positive values of RE and

CE).

With the aim of answering the second thesis question, this thesis examined the uniqueness of changes in the observational record by comparing it to the reconstructed station pressure variability and trends from 1905-2011. Special interest was focused on the second half of the 20th century when forcing from stratospheric ozone depletion and greenhouse gases has become more significant. Several methods were used to place the 146 recent changes and variability in the more historical context now afforded by the reconstructions; namely, a 7-/ 11-year smoothing of the data and a comparison of decadal means to highlight low-frequency variability; and comparison of trends, using both fixed

(1905-1956 vs. 1957-2011) and temporally-varying time periods (30-/40-/50-year trends) to examine overall changes in the pressure at each station.

In austral summer, the key important finding is that the lowest station pressure is found from 1990-2010 (mainly in East Antarctica), where the decreasing trend during

1957-2011 is shown to be both statistically significant (p<0.05) and unique. Hence, this unprecedented historical trend in the 100+ year reconstructed record is believed to be related to the stratospheric ozone depletion and/or greenhouse gases. Furthermore, West

Antarctic stations have shown that overall station pressure appears to be the highest around the 1960s-1970s, although this finding is only supported by the two low- frequency variability tests.

In austral autumn, a similar result as in DJF was also observed, where the 1990-

2010 decade also is characterized with the lowest station pressure among the East

Antarctic stations. In terms of trends, both trend analyses are able to depict a significant negative trend in the second half of the 20th century in East Antarctic stations. These findings indicate that stratospheric ozone depletion and/or greenhouse gases are likely to continue to influence the pressure of the majority of Antarctic stations. Nevertheless, an interesting feature is also found in East Antarctica. In both the decadal mean test and 7-

/11- smoothed method, East Antarctic stations appear to be similarly low during the

1930s, although this only happens in a short period of time and is not as persistent as the 147 trend that occurred in the second half of the 20th century. In contrast, a different story is seen in West Antarctica, in which the 1960s is the decade of the lowest station pressure in the 100+ years of the reconstruction. Since these special events only occurred in the

Antarctic Peninsula, it is believed that West Antarctic Peninsula stations were influenced by another forcing, such as perhaps a stronger Amundsen-Bellingshausen Seas Low

(Hosking et al. 2013).

In austral winter, specifically in East Antarctica, both low-frequency variability tests have shown the 1930s (1960s-1970s) were the decades that have the lowest

(highest) decadal mean pressure, which can also be seen in the 30-year smoothed trends.

Nevertheless, these trends tend to be short. In West Antarctica, the 1990s seem to have the lowest decadal mean station pressure in 100+ years, although this trend is also relatively short and not as significant as the trends that are seen in austral summer and autumn. Notably, a shift towards the trend becoming more positive is shown from 1970-

2011 in West Antarctica, which is concurrent and consistent with the station pressure that peaked there in the 2000-2010 decade. However, trends in this season across the West

Antarctic stations are insignificant in most of the stations. Hence, the shift in trend magnitude that occurred from 1970-2011 might have been highly influenced by some alternative forcing, which is still unclear at the present stage.

In austral spring, both low-frequency variability tests have shown that the 1930s was the decade that had the lowest decadal mean pressure in East Antarctica, whereas

2000s was the decade that had the highest decadal mean pressure in West Antarctica. 148

In terms of historical station pressure variability, the Antarctic Peninsula stations have shown a statistically significant (p<0.05) positive trend during 1957-2011. This significant positive trend can also be seen in persistent positive 30-/40-year running trends over the last several decades. Furthermore, a negative trend is found and supported by the low decadal mean pressure in the 1930s in East Antarctica. Interestingly, among

MAM and JJA, a consistent report of the lowest decadal mean pressures occurred in East

Antarctica during the 1930s; th is local pressure minima is also observed in West

Antarctica during austral spring. Notably, the continuation of a strong and unique shift towards a more positive trend in West Antarctica from 1970-2011 might again be affected by similar forcing as austral winter. Further research is needed to understand the causality of the changes in pressure recently in West Antarctica during austral spring.

Examining the reconstruction records has proved that the PCR model yields a better reconstruction skill than using the climatological mean alone. Nevertheless, there are still many possible ways that might perhaps be able to enhance the overall reconstruction skill. A possible improvement would be to use observation records without any attempt to patch the missing years, since patched data might introduce errors, such as outliers, that could reduce the reconstruction skill. Furthermore, when performing the

PCR model, if one of the predictor years is missing, a researcher might perhaps still consider including that year rather than discarding it for model calibration; this might provide better calibration and validation statistics (particular for the shorter 30-year calibrations) since more years will be included in the model training. Lastly, additional improvement would be to allow for different predictor networks for the 30-year 149 calibrations, rather than simply using the same networks employed in the full-period calibrations. In this approach, the first 30 years (1957-1986) in the early period and the last 30 years (1982-2011) in the late period would be used individually to obtain separate p<0.05 and p<0.10 networks. These might perhaps be able to find more significant predictors that have a strong relationship with the particular years (i.e., first 30 years or last 30 years), while removing stations whose relationship with the predictand is not temporally consistent.

It is the author‟s desire that the discussion presented in this thesis has provided a more complete Antarctic station pressure record, as these new reconstructions nearly doubles the length of most of the observed records, extending them back to 1905. These longer records have already helped to assess the uniqueness of the ongoing Antarctic climate in the second half of the 20th century, and have the potential to be used in many other attribution analyses as well as lower boundary constraints for models and reanalysis across Antarctica. Overall, evidence has shown that the period when the stratospheric ozone depletion and/or greenhouse gases are the strongest appears to have a significant influence on Antarctic station pressure. Therefore, future research should investigate more precisely the relative roles of stratospheric ozone depletion, greenhouse gas increases, and natural variability in driving Antarctic atmospheric circulation changes.

Notwithstanding the challenges to interpreting the recent changes with only a short reliable data record and multiple forcing mechanisms, it is through work like this thesis that a more thorough understanding of Antarctic climate will be made possible and achievable. 150

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APPENDIX: STATION PRESSURE RECONSTRUCTIONS IN 5% AND 10% PREDICTOR NETWORKS IN ALL SEASONS DJF p<0.05; Full (Calibration and validation periods 1957-2010) Predictand Predictors Stations #f Crit Corr Cal r Val r RE CE years years Amundsen-Scott 4 0.210 0.760 0.750 0.686 0.667 54 44 Bellingshausen 7 0.130 0.830 0.733 0.761 0.652 52 43 Byrd 8 0.050 0.730 0.636 0.561 0.441 44 44 Casey 9 0.115 0.794 0.746 0.749 0.675 51 43 Davis 11 0.100 0.776 0.638 0.752 0.601 49 43 Dumont d'Urville 6 0.121 0.816 0.779 0.750 0.685 54 42 Esperanza 9 0.100 0.874 0.784 0.805 0.667 54 44 Faraday 6 0.100 0.856 0.800 0.776 0.689 54 45 Halley 9 0.110 0.826 0.752 0.817 0.695 54 43 Marambio 3 0.100 0.706 0.615 0.589 0.524 40 49 Marsh/ O'Higgins 6 0.100 0.787 0.699 0.615 0.544 46 53 Mawson 8 0.140 0.759 0.691 0.716 0.595 54 43 McMurdo/ Scott base 5 0.200 0.772 0.740 0.706 0.651 54 43 Mirny 3 0.170 0.641 0.581 0.613 0.525 54 45 Novolazarevskaya 6 0.170 0.729 0.675 0.808 0.730 50 43 Rothera 6 0.100 0.825 0.764 0.743 0.647 51 45 Syowa 4 0.200 0.652 0.586 0.638 0.567 50 44 Vostok 4 0.220 0.739 0.672 0.703 0.635 51 44

Earlier (Calibrate period 1957-1986; Validation period 1987-2010) Predictand Predictors Stations #f Crit Corr Cal r Val r RE CE years years Amundsen-Scott 4 0.230 0.862 0.754 0.610 0.515 30 28 Bellingshausen 6 0.200 0.855 0.682 0.461 0.352 28 28 Byrd 9 0.150 0.666 0.671 0.392 0.384 24 29 Casey 2 0.200 0.910 0.610 0.539 0.321 27 28 Davis 9 0.150 0.879 0.513 0.473 0.128 25 28 Dumont d'Urville 4 0.310 0.870 0.655 0.624 0.382 30 28 Esperanza 4 0.150 0.925 0.757 0.459 0.388 30 29 Faraday 4 0.200 0.902 0.849 0.728 0.666 30 29 Halley 3 0.280 0.881 0.676 0.690 0.465 30 28 Marambio 4 0.110 0.597 0.674 0.510 0.389 17 30 Marsh/ O'Higgins 8 0.010 0.701 0.859 0.704 0.575 22 30 Mawson 7 0.180 0.915 0.710 0.614 0.403 30 28 McMurdo/ Scott base 4 0.260 0.902 0.747 0.648 0.499 30 28 Mirny 2 0.200 0.721 0.759 0.723 0.480 30 29 Novolazarevskaya 5 0.150 0.949 0.499 0.690 0.234 26 28 Rothera 2 0.440 0.838 0.861 0.732 0.609 27 29 Syowa 3 0.250 0.741 0.654 0.596 0.387 26 28 Vostok 2 0.350 0.739 0.746 0.553 0.414 27 28

Late (Calibrate period 1982-2010; Validation period 1957-1981) Predictand Predictors Stations #f Crit Corr Cal r Val r RE CE years years Amundsen-Scott 6 0.260 0.750 0.861 0.727 0.720 29 21 Bellingshausen 7 0.199 0.900 0.688 0.434 0.208 29 20 Byrd 2 0.270 0.751 0.885 0.577 0.572 25 20 Casey 9 0.100 0.750 0.867 0.751 0.711 29 20 Davis 4 0.300 0.787 0.828 0.694 0.643 29 20 Dumont d'Urville 2 0.280 0.698 0.885 0.780 0.764 29 19 Esperanza 9 0.100 0.826 0.854 0.728 0.671 29 20 Faraday 3 0.400 0.871 0.759 0.585 0.420 29 21 Halley 4 0.340 0.768 0.893 0.828 0.760 29 20 Marambio 2 0.400 0.808 0.937 0.554 0.341 28 24 Marsh/ O'Higgins 7 0.240 0.919 0.684 0.415 -0.202 29 28 Mawson 2 0.320 0.629 0.845 0.756 0.691 29 20 McMurdo/ Scott base 2 0.350 0.716 0.903 0.822 0.786 29 20 Mirny 4 0.250 0.493 0.797 0.779 0.577 29 21 Novolazarevskaya 3 0.316 0.693 0.873 0.822 0.708 29 20 Rothera 3 0.350 0.772 0.837 0.757 0.580 29 21 Syowa 11 0.080 0.787 0.692 0.568 0.424 29 21 Vostok 8 0.150 0.800 0.774 0.595 0.551 29 21

158

DJF p<0.10; Full (Calibration and validation periods 1957-2010) Predictand Predictors Stations #f Crit Corr Cal r Val r RE CE years years Amundsen-Scott 7 0.170 0.785 0.741 0.710 0.633 54 42 Bellingshausen 7 0.130 0.830 0.733 0.761 0.652 52 43 Byrd 10 0.080 0.759 0.677 0.586 0.487 44 44 Casey 9 0.115 0.794 0.746 0.749 0.675 51 43 Davis 5 0.200 0.721 0.640 0.663 0.579 49 42 Dumont d'Urville 8 0.134 0.806 0.761 0.745 0.656 54 42 Esperanza 11 0.050 0.871 0.77 0.801 0.639 54 44 Faraday 6 0.160 0.850 0.802 0.782 0.699 54 44 Halley 5 0.200 0.772 0.729 0.770 0.694 54 42 Marambio 5 0.200 0.706 0.560 0.592 0.465 40 49 Marsh/ O'Higgins 6 0.100 0.791 0.712 0.633 0.566 46 52 Mawson 7 0.160 0.731 0.688 0.684 0.602 54 43 McMurdo/ Scott base 5 0.200 0.770 0.726 0.716 0.638 29 19 Mirny 5 0.160 0.688 0.632 0.668 0.583 54 44 Novolazarevskaya 6 0.170 0.729 0.675 0.808 0.730 50 43 Rothera 7 0.100 0.823 0.755 0.736 0.632 51 45 Syowa 4 0.220 0.647 0.595 0.632 0.576 50 44 Vostok 4 0.210 0.704 0.649 0.666 0.612 51 44

Earlier (Calibrate period 1957-1986; Validation period 1987-2010) Predictand Predictors Stations #f Crit Corr Cal r Val r RE CE years years Amundsen-Scott 2 0.350 0.851 0.720 0.593 0.494 30 28 Bellingshausen 6 0.200 0.855 0.682 0.461 0.352 28 28 Byrd 8 0.200 0.747 0.719 0.473 0.466 24 29 Casey 2 0.200 0.910 0.610 0.539 0.321 27 28 Davis 1 0.430 0.870 0.512 0.538 0.235 25 28 Dumont d'Urville 1 0.410 0.876 0.572 0.572 0.295 30 28 Esperanza 4 0.150 0.926 0.751 0.456 0.385 30 29 Faraday 5 0.150 0.919 0.793 0.655 0.576 30 29 Halley 2 0.280 0.874 0.691 0.696 0.476 30 28 Marambio 5 0.070 0.622 0.697 0.545 0.433 17 30 Marsh/ O'Higgins 9 0.010 0.700 0.856 0.699 0.567 22 30 Mawson 5 0.194 0.906 0.631 0.570 0.335 30 28 McMurdo/ Scott base 2 0.300 0.880 0.727 0.634 0.480 30 28 Mirny 2 0.400 0.703 0.719 0.700 0.437 30 28 Novolazarevskaya 5 0.150 0.949 0.499 0.690 0.234 26 28 Rothera 3 0.200 0.839 0.861 0.716 0.585 27 29 Syowa 2 0.400 0.725 0.562 0.543 0.307 26 28 Vostok 6 0.230 0.809 0.749 0.509 0.357 27 28

Late (Calibrate period 1982-2010; Validation period 1957-1981) Predictand Predictors Stations #f Crit Corr Cal r Val r RE CE years years Amundsen-Scott 3 0.388 0.730 0.882 0.761 0.747 29 19 Bellingshausen 7 0.199 0.900 0.688 0.434 0.208 29 20 Byrd 2 0.400 0.716 0.881 0.626 0.621 25 20 Casey 9 0.100 0.750 0.867 0.751 0.711 29 20 Davis 9 0.150 0.753 0.890 0.808 0.777 29 19 Dumont d'Urville 2 0.280 0.701 0.877 0.776 0.760 29 19 Esperanza 5 0.280 0.822 0.853 0.748 0.695 29 20 Faraday 3 0.400 0.802 0.884 0.761 0.666 29 20 Halley 1 0.580 0.654 0.912 0.835 0.771 29 19 Marambio 7 0.114 0.838 0.948 0.534 0.310 28 24 Marsh/ O'Higgins 4 0.300 0.832 0.786 0.647 0.276 29 27 Mawson 3 0.330 0.622 0.858 0.778 0.719 29 20 McMurdo/ Scott base 2 0.400 0.738 0.889 0.788 0.745 29 19 Mirny 8 0.200 0.498 0.696 0.711 0.447 29 21 Novolazarevskaya 3 0.316 0.693 0.873 0.822 0.708 29 20 Rothera 4 0.300 0.773 0.748 0.625 0.350 29 21 Syowa 7 0.280 0.661 0.796 0.716 0.620 29 21 Vostok 3 0.441 0.741 0.867 0.750 0.723 29 21

159

MAM p<0.05; Full (Calibration and validation periods 1957-2011) Predictand Predictors Stations #f Crit Corr Cal r Val r RE CE years years Amundsen-Scott 6 0.120 0.631 0.560 0.448 0.344 55 49 Bellingshausen 3 0.100 0.814 0.774 0.714 0.653 53 54 Byrd 1 0.160 0.586 0.537 0.344 0.288 44 47 Casey 4 0.110 0.441 0.326 0.256 0.130 52 49 Davis 4 0.090 0.568 0.512 0.440 0.362 51 49 Dumont d'Urville 4 0.150 0.655 0.603 0.437 0.358 55 48 Esperanza 3 0.100 0.735 0.695 0.594 0.534 55 55 Faraday 3 0.100 0.784 0.743 0.643 0.576 55 54 Halley 5 0.145 0.563 0.482 0.355 0.253 55 49 Marambio 3 0.120 0.725 0.670 0.637 0.586 41 53 Marsh/ O'Higgins 3 0.050 0.727 0.669 0.642 0.569 48 54 Mawson 3 0.150 0.605 0.585 0.459 0.437 55 48 McMurdo/ Scott base 8 0.050 0.591 0.461 0.458 0.258 55 49 Mirny 4 0.100 0.470 0.409 0.428 0.338 55 50 Novolazarevskaya 5 0.150 0.609 0.579 0.517 0.472 51 50 Rothera 2 0.050 0.615 0.570 0.411 0.355 52 54 Syowa 3 0.120 0.531 0.466 0.361 0.290 50 49 Vostok 7 0.090 0.632 0.561 0.461 0.351 52 47

Earlier (Calibrate period 1957-1986; Validation period 1987-2011) Predictand Predictors Stations #f Crit Corr Cal r Val r RE CE years years Amundsen-Scott 1 0.300 0.461 0.516 0.379 0.262 30 29 Bellingshausen 2 0.050 0.865 0.796 0.661 0.645 28 29 Byrd 1 0.260 0.643 0.604 0.285 0.279 21 29 Casey 1 0.120 0.454 0.349 0.204 0.119 27 30 Davis 1 0.300 0.485 0.617 0.485 0.327 26 30 Dumont d'Urville 1 0.300 0.501 0.560 0.326 0.278 30 29 Esperanza 3 0.050 0.792 0.648 0.418 0.405 30 30 Faraday 2 0.020 0.861 0.694 0.476 0.471 30 29 Halley 2 0.300 0.509 0.566 0.366 0.303 30 30 Marambio 2 0.240 0.765 0.668 0.381 0.381 16 30 Marsh/ O'Higgins 3 0.050 0.718 0.832 0.597 0.596 23 30 Mawson 1 0.250 0.489 0.668 0.515 0.391 30 29 McMurdo/ Scott base 2 0.300 0.614 0.502 0.452 0.247 30 29 Mirny 4 0.100 0.363 0.572 0.648 0.295 30 30 Novolazarevskaya 5 0.150 0.702 0.602 0.512 0.290 26 30 Rothera 2 0.150 0.713 0.435 0.250 0.233 27 29 Syowa 1 0.350 0.394 0.607 0.379 0.293 25 30 Vostok 1 0.330 0.545 0.618 0.408 0.361 28 28

Late (Calibrate period 1982-2011; Validation period 1957-1981) Predictand Predictors Stations #f Crit Corr Cal r Val r RE CE years years Amundsen-Scott 3 0.300 0.626 0.545 0.354 0.295 30 25 Bellingshausen 2 0.060 0.773 0.820 0.697 0.675 30 29 Byrd 1 0.370 0.574 0.733 0.418 0.409 28 22 Casey 2 0.230 0.487 0.278 0.120 0.028 30 24 Davis 3 0.330 0.553 0.631 0.543 0.337 30 24 Dumont d'Urville 9 0.100 0.605 0.694 0.405 0.358 30 24 Esperanza 3 0.030 0.791 0.737 0.592 0.578 30 30 Faraday 3 0.090 0.757 0.826 0.703 0.692 30 29 Halley 2 0.300 0.528 0.465 0.277 0.190 30 24 Marambio 2 0.200 0.756 0.935 0.859 0.856 30 28 Marsh/ O'Higgins 4 0.100 0.806 0.754 0.640 0.623 30 29 Mawson 1 0.510 0.593 0.588 0.472 0.287 30 24 McMurdo/ Scott base 4 0.210 0.391 0.610 0.428 0.171 30 25 Mirny 2 0.300 0.515 0.236 0.549 -0.010 30 25 Novolazarevskaya 7 0.150 0.703 0.651 0.531 0.384 30 25 Rothera 2 0.170 0.613 0.688 0.498 0.483 30 29 Syowa 1 0.400 0.490 0.532 0.415 0.247 30 24 Vostok 8 0.150 0.554 0.731 0.499 0.465 29 23

160

MAM p<0.10; Full (Calibration and validation periods 1957-2011) Predictand Predictors Stations #f Crit Corr Cal r Val r RE CE years years Amundsen-Scott 6 0.1500 0.606 0.571 0.422 0.344 55 47 Bellingshausen 5 0.1000 0.839 0.796 0.744 0.676 53 53 Byrd 1 0.2000 0.567 0.514 0.337 0.282 44 47 Casey 7 0.1700 0.454 0.413 0.268 0.183 52 48 Davis 2 0.2000 0.555 0.532 0.387 0.361 51 48 Dumont d'Urville 5 0.1400 0.660 0.606 0.441 0.353 55 48 Esperanza 3 0.1300 0.759 0.713 0.612 0.542 55 53 Faraday 4 0.1000 0.781 0.732 0.642 0.561 55 54 Halley 6 0.0600 0.590 0.462 0.407 0.236 55 49 Marambio 3 0.120 0.725 0.670 0.637 0.586 41 53 Marsh/ O'Higgins 4 0.1300 0.784 0.736 0.683 0.620 48 53 Mawson 3 0.1800 0.605 0.584 0.457 0.432 55 48 McMurdo/ Scott base 5 0.1110 0.551 0.455 0.417 0.290 55 48 Mirny 5 0.1800 0.471 0.400 0.466 0.369 55 47 Novolazarevskaya 6 0.2000 0.677 0.642 0.555 0.492 51 47 Rothera 3 0.1500 0.627 0.539 0.417 0.309 52 53 Syowa 3 0.1500 0.555 0.493 0.399 0.331 50 49 Vostok 2 0.2000 0.576 0.554 0.381 0.351 52 47

Earlier (Calibrate period 1957-1986; Validation period 1987-2011) Predictand Predictors Stations #f Crit Corr Cal r Val r RE CE years years Amundsen-Scott 2 0.3500 0.679 0.637 0.337 0.212 30 29 Bellingshausen 3 0.2800 0.856 0.814 0.695 0.681 28 29 Byrd 1 0.2000 0.622 0.584 0.265 0.259 21 29 Casey 1 0.3200 0.456 0.226 0.131 0.038 27 30 Davis 1 0.3200 0.454 0.592 0.466 0.303 26 29 Dumont d'Urville 1 0.3000 0.451 0.526 0.289 0.238 30 29 Esperanza 1 0.3300 0.742 0.744 0.461 0.449 30 30 Faraday 2 0.1000 0.865 0.680 0.461 0.455 30 29 Halley 3 0.2500 0.546 0.606 0.415 0.357 30 30 Marambio 2 0.240 0.765 0.668 0.381 0.381 16 30 Marsh/ O'Higgins 3 0.1100 0.730 0.823 0.683 0.682 23 29 Mawson 3 0.1873 0.548 0.603 0.471 0.335 30 29 McMurdo/ Scott base 1 0.4000 0.484 0.509 0.440 0.230 30 29 Mirny 4 0.2000 0.361 0.600 0.670 0.340 30 29 Novolazarevskaya 2 0.2700 0.442 0.611 0.529 0.315 26 29 Rothera 3 0.2000 0.705 0.463 0.303 0.288 27 29 Syowa 1 0.3000 0.428 0.626 0.400 0.317 25 30 Vostok 1 0.4000 0.586 0.630 0.420 0.374 28 28

Late (Calibrate period 1982-2011; Validation period 1957-1981) Predictand Predictors Stations #f Crit Corr Cal r Val r RE CE years years Amundsen-Scott 3 0.3000 0.652 0.684 0.405 0.351 30 23 Bellingshausen 3 0.1000 0.841 0.773 0.696 0.674 30 28 Byrd 1 0.3700 0.565 0.777 0.417 0.408 28 22 Casey 2 0.3000 0.628 0.111 -0.108 -0.223 30 23 Davis 5 0.2500 0.544 0.655 0.590 0.405 30 24 Dumont d'Urville 13 0.0500 0.604 0.699 0.343 0.291 30 24 Esperanza 5 0.1000 0.807 0.741 0.598 0.584 30 28 Faraday 2 0.2600 0.733 0.806 0.670 0.657 30 29 Halley 2 0.3100 0.553 0.466 0.281 0.194 30 24 Marambio 2 0.200 0.756 0.935 0.859 0.856 30 28 Marsh/ O'Higgins 4 0.2300 0.822 0.796 0.685 0.671 30 28 Mawson 1 0.4900 0.561 0.569 0.449 0.255 30 24 McMurdo/ Scott base 2 0.4000 0.391 0.732 0.568 0.373 30 24 Mirny 8 0.1000 0.586 0.354 0.595 0.092 30 23 Novolazarevskaya 11 0.1700 0.714 0.604 0.513 0.359 30 23 Rothera 3 0.3000 0.614 0.709 0.536 0.522 30 28 Syowa 4 0.2510 0.556 0.530 0.423 0.259 30 24 Vostok 8 0.1700 0.547 0.731 0.422 0.383 29 23

161

JJA p<0.05; Full (Calibration and validation periods 1957-2011) Predictand Predictors Stations #f Crit Corr Cal r Val r RE CE years years Amundsen-Scott 4 0.170 0.602 0.491 0.378 0.236 55 50 Bellingshausen 8 0.030 0.916 0.885 0.837 0.776 53 46 Byrd 3 0.200 0.581 0.447 0.332 0.223 41 51 Casey 5 0.171 0.749 0.678 0.578 0.473 52 50 Davis 6 0.146 0.650 0.538 0.515 0.362 51 50 Dumont d'Urville 7 0.175 0.714 0.616 0.533 0.374 55 49 Esperanza 4 0.100 0.835 0.812 0.697 0.659 55 53 Faraday 4 0.100 0.862 0.847 0.737 0.710 55 47 Halley 4 0.200 0.688 0.615 0.451 0.350 55 50 Marambio 5 0.100 0.772 0.731 0.669 0.636 41 47 Marsh/ O'Higgins 8 0.010 0.868 0.810 0.797 0.721 47 46 Mawson 5 0.210 0.591 0.487 0.380 0.249 55 52 McMurdo/ Scott base 5 0.130 0.701 0.551 0.508 0.288 55 50 Mirny 7 0.150 0.674 0.573 0.575 0.416 55 50 Novolazarevskaya 8 0.100 0.699 0.557 0.503 0.290 51 50 Rothera 6 0.100 0.784 0.729 0.646 0.570 53 46 Syowa 5 0.120 0.577 0.431 0.361 0.187 50 51 Vostok 6 0.150 0.700 0.612 0.494 0.373 52 50

Earlier (Calibrate period 1957-1986; Validation period 1987-2011) Predictand Predictors Stations #f Crit Corr Cal r Val r RE CE years years Amundsen-Scott 1 0.300 0.480 0.385 0.148 0.144 30 29 Bellingshausen 5 0.140 0.920 0.895 0.780 0.778 28 30 Byrd 2 0.200 0.607 0.649 0.495 0.033 21 30 Casey 3 0.260 0.779 0.658 0.421 0.421 27 29 Davis 5 0.200 0.743 0.498 0.296 0.177 26 29 Dumont d'Urville 2 0.300 0.722 0.636 0.376 0.369 30 29 Esperanza 4 0.100 0.879 0.764 0.589 0.589 30 30 Faraday 4 0.220 0.881 0.823 0.654 0.653 30 30 Halley 3 0.300 0.822 0.346 -0.152 -0.246 30 29 Marambio 2 0.280 0.725 0.764 0.485 0.478 16 30 Marsh/ O'Higgins 7 0.020 0.841 0.940 0.806 0.802 23 30 Mawson 3 0.196 0.637 0.430 0.151 0.117 30 29 McMurdo/ Scott base 2 0.290 0.693 0.620 0.283 0.279 30 29 Mirny 3 0.240 0.745 0.601 0.436 0.269 30 29 Novolazarevskaya 3 0.300 0.750 0.511 0.359 0.169 26 29 Rothera 3 0.300 0.666 0.813 0.614 0.613 28 30 Syowa 2 0.300 0.646 0.432 0.111 0.109 25 30 Vostok 2 0.300 0.686 0.651 0.282 0.249 28 29

Late (Calibrate period 1982-2011; Validation period 1957-1981) Predictand Predictors Stations #f Crit Corr Cal r Val r RE CE years years Amundsen-Scott 5 0.260 0.586 0.585 0.364 0.336 30 26 Bellingshausen 5 0.300 0.926 0.901 0.700 0.693 30 21 Byrd 1 0.240 0.540 0.719 0.399 0.209 25 26 Casey 7 0.200 0.690 0.757 0.527 0.517 30 26 Davis 6 0.200 0.602 0.705 0.505 0.339 30 26 Dumont d'Urville 6 0.300 0.716 0.671 0.446 0.393 30 25 Esperanza 5 0.100 0.806 0.793 0.648 0.624 30 28 Faraday 5 0.200 0.877 0.809 0.646 0.633 30 22 Halley 4 0.193 0.425 0.676 0.368 0.276 30 26 Marambio 4 0.200 0.818 0.871 0.720 0.713 30 22 Marsh/ O'Higgins 7 0.193 0.940 0.814 0.542 0.540 29 21 Mawson 7 0.100 0.588 0.479 0.148 0.008 30 28 McMurdo/ Scott base 7 0.200 0.664 0.708 0.514 0.423 30 26 Mirny 7 0.136 0.608 0.622 0.527 0.265 30 26 Novolazarevskaya 5 0.220 0.676 0.780 0.612 0.470 30 26 Rothera 5 0.210 0.852 0.680 0.468 0.440 30 21 Syowa 5 0.200 0.573 0.426 0.047 0.039 30 26 Vostok 4 0.300 0.631 0.673 0.415 0.412 29 26

162

JJA p<0.10; Full (Calibration and validation periods 1957-2011) Predictand Predictors Stations #f Crit Corr Cal r Val r RE CE years years Amundsen-Scott 7 0.140 0.667 0.533 0.470 0.271 55 54 Bellingshausen 8 0.030 0.916 0.885 0.837 0.776 53 46 Byrd 7 0.082 0.652 0.482 0.385 0.274 41 46 Casey 8 0.140 0.742 0.639 0.573 0.406 52 49 Davis 7 0.150 0.683 0.566 0.538 0.359 51 49 Dumont d'Urville 8 0.160 0.720 0.600 0.540 0.346 55 49 Esperanza 6 0.090 0.840 0.815 0.706 0.664 55 47 Faraday 5 0.200 0.868 0.848 0.746 0.709 55 46 Halley 4 0.200 0.688 0.615 0.451 0.350 55 50 Marambio 5 0.227 0.800 0.743 0.726 0.701 41 47 Marsh/ O'Higgins 8 0.010 0.868 0.810 0.797 0.721 47 46 Mawson 7 0.120 0.654 0.512 0.447 0.246 55 50 McMurdo/ Scott base 4 0.200 0.687 0.546 0.493 0.287 55 50 Mirny 5 0.163 0.648 0.563 0.532 0.414 55 49 Novolazarevskaya 7 0.135 0.719 0.610 0.543 0.375 51 49 Rothera 6 0.100 0.784 0.729 0.646 0.570 53 46 Syowa 5 0.160 0.574 0.423 0.376 0.220 50 50 Vostok 7 0.130 0.706 0.612 0.508 0.374 52 50

Earlier (Calibrate period 1957-1986; Validation period 1987-2011) Predictand Predictors Stations #f Crit Corr Cal r Val r RE CE years years Amundsen-Scott 1 0.430 0.477 0.363 0.130 0.127 30 29 Bellingshausen 5 0.140 0.920 0.895 0.780 0.778 28 30 Byrd 2 0.250 0.624 0.632 0.488 0.021 21 30 Casey 4 0.200 0.817 0.643 0.380 0.380 27 29 Davis 7 0.130 0.794 0.495 0.263 0.138 26 29 Dumont d'Urville 2 0.310 0.647 0.714 0.473 0.466 30 29 Esperanza 3 0.400 0.836 0.745 0.592 0.591 30 30 Faraday 4 0.210 0.881 0.813 0.631 0.629 30 30 Halley 3 0.300 0.822 0.346 -0.152 -0.246 30 29 Marambio 7 0.001 0.715 0.766 0.543 0.537 16 30 Marsh/ O'Higgins 7 0.020 0.841 0.940 0.806 0.802 23 30 Mawson 12 0.006 0.795 0.486 0.153 0.119 30 29 McMurdo/ Scott base 3 0.273 0.715 0.653 0.342 0.338 30 29 Mirny 2 0.300 0.700 0.600 0.481 0.327 30 29 Novolazarevskaya 6 0.100 0.855 0.603 0.273 0.058 26 29 Rothera 3 0.300 0.666 0.813 0.614 0.613 28 30 Syowa 2 0.360 0.638 0.421 0.101 0.099 25 29 Vostok 2 0.300 0.685 0.654 0.271 0.237 28 29

Late (Calibrate period 1982-2011; Validation period 1957-1981) Predictand Predictors Stations #f Crit Corr Cal r Val r RE CE years years Amundsen-Scott 6 0.200 0.591 0.532 0.291 0.260 30 30 Bellingshausen 5 0.300 0.926 0.901 0.700 0.693 30 21 Byrd 9 0.090 0.702 0.731 0.300 0.079 25 21 Casey 4 0.330 0.632 0.769 0.434 0.422 30 25 Davis 6 0.170 0.588 0.712 0.501 0.334 30 25 Dumont d'Urville 9 0.153 0.792 0.584 0.336 0.271 30 25 Esperanza 4 0.200 0.812 0.859 0.752 0.735 30 22 Faraday 4 0.300 0.843 0.800 0.616 0.602 30 21 Halley 4 0.193 0.425 0.676 0.368 0.276 30 26 Marambio 2 0.370 0.834 0.911 0.695 0.687 30 22 Marsh/ O'Higgins 7 0.193 0.940 0.814 0.542 0.540 29 21 Mawson 6 0.120 0.591 0.589 0.334 0.225 30 26 McMurdo/ Scott base 5 0.230 0.655 0.721 0.532 0.444 30 26 Mirny 5 0.200 0.554 0.706 0.575 0.339 30 25 Novolazarevskaya 7 0.200 0.725 0.798 0.646 0.517 30 25 Rothera 5 0.210 0.852 0.680 0.468 0.440 30 21 Syowa 1 0.370 0.472 0.308 0.078 0.070 30 26 Vostok 5 0.200 0.649 0.680 0.407 0.403 29 26

163

SON p<0.05; Full (Calibration and validation periods 1957-2011) Predictand Predictors Stations #f Crit Corr Cal r Val r RE CE years years Amundsen-Scott 2 0.220 0.535 0.462 0.197 0.110 55 48 Bellingshausen 4 0.100 0.808 0.758 0.733 0.652 53 52 Byrd 3 0.180 0.648 0.559 0.225 0.115 39 47 Casey 4 0.100 0.618 0.551 0.307 0.216 52 52 Davis 2 0.200 0.549 0.481 0.343 0.259 51 46 Dumont d'Urville 10 0.050 0.638 0.435 0.407 0.128 55 49 Esperanza 2 0.100 0.699 0.666 0.568 0.516 55 51 Faraday 1 0.100 0.741 0.721 0.589 0.555 55 54 Halley 6 0.100 0.646 0.454 0.429 0.184 55 51 Marambio 2 0.100 0.573 0.450 0.481 0.380 41 54 Marsh/ O'Higgins 2 0.100 0.672 0.588 0.576 0.482 47 54 Mawson 3 0.100 0.565 0.486 0.307 0.204 55 46 McMurdo/ Scott base 4 0.100 0.655 0.550 0.427 0.291 55 52 Mirny 4 0.140 0.574 0.513 0.368 0.278 55 53 Novolazarevskaya 2 0.150 0.527 0.448 0.270 0.184 51 46 Rothera 1 0.100 0.588 0.531 0.435 0.375 52 54 Syowa 2 0.210 0.604 0.550 0.355 0.288 50 46 Vostok 4 0.118 0.557 0.424 0.262 0.116 52 51

Earlier (Calibrate period 1957-1986; Validation period 1987-2011) Predictand Predictors Stations #f Crit Corr Cal r Val r RE CE years years Amundsen-Scott 1 0.430 0.500 0.431 0.254 -0.344 30 29 Bellingshausen 3 0.100 0.883 0.874 0.666 0.666 28 30 Byrd 3 0.300 0.606 0.728 0.512 0.041 21 28 Casey 3 0.178 0.655 0.665 0.330 0.231 27 30 Davis 1 0.280 0.409 0.724 0.414 0.408 26 29 Dumont d'Urville 1 0.300 0.513 0.473 0.209 0.180 30 28 Esperanza 2 0.100 0.721 0.764 0.598 0.590 30 30 Faraday 1 0.120 0.837 0.805 0.562 0.561 30 30 Halley 1 0.403 0.326 0.597 0.327 0.239 30 29 Marambio 2 0.100 0.309 0.720 0.180 0.180 17 30 Marsh/ O'Higgins 1 0.100 0.644 0.862 0.578 0.578 23 30 Mawson 1 0.360 0.421 0.667 0.415 0.374 30 29 McMurdo/ Scott base 1 0.340 0.493 0.661 0.414 0.374 30 29 Mirny 4 0.100 0.638 0.653 0.317 0.314 30 30 Novolazarevskaya 1 0.200 0.439 0.646 0.393 0.331 26 29 Rothera 1 0.230 0.590 0.695 0.384 0.372 27 30 Syowa 1 0.370 0.566 0.701 0.483 0.365 25 29 Vostok 1 0.330 0.306 0.545 0.391 0.028 28 29

Late (Calibrate period 1982-2011; Validation period 1957-1981) Predictand Predictors Stations #f Crit Corr Cal r Val r RE CE years years Amundsen-Scott 4 0.100 0.578 0.353 0.014 -0.057 30 24 Bellingshausen 2 0.340 0.748 0.935 0.802 0.801 30 27 Byrd 2 0.400 0.663 0.676 0.481 0.012 23 24 Casey 2 0.300 0.574 0.726 0.318 0.314 30 27 Davis 1 0.480 0.617 0.379 0.144 0.097 30 22 Dumont d'Urville 3 0.200 0.365 0.662 0.305 0.297 30 26 Esperanza 2 0.100 0.752 0.760 0.524 0.517 30 26 Faraday 1 0.340 0.738 0.851 0.608 0.600 30 29 Halley 4 0.150 0.703 0.581 0.062 0.059 30 27 Marambio 2 0.100 0.767 0.567 0.206 0.203 29 29 Marsh/ O'Higgins 2 0.100 0.720 0.900 0.539 0.524 29 29 Mawson 1 0.460 0.615 0.343 0.126 0.121 30 22 McMurdo/ Scott base 5 0.121 0.672 0.581 0.300 0.274 30 28 Mirny 6 0.008 0.596 0.497 0.378 0.213 30 28 Novolazarevskaya 1 0.380 0.624 0.269 -0.019 -0.030 30 22 Rothera 1 0.410 0.615 0.723 0.475 0.423 30 29 Syowa 1 0.550 0.681 0.469 0.222 0.197 30 22 Vostok 3 0.240 0.510 0.384 0.181 0.007 29 27

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SON p<0.10; Full (Calibration and validation periods 1957-2011) Predictand Predictors Stations #f Crit Corr Cal r Val r RE CE years years Amundsen-Scott 3 0.200 0.537 0.450 0.190 0.081 55 48 Bellingshausen 4 0.100 0.808 0.758 0.733 0.652 53 52 Byrd 3 0.200 0.647 0.562 0.163 0.031 39 45 Casey 10 0.050 0.698 0.529 0.461 0.224 52 51 Davis 3 0.100 0.623 0.545 0.405 0.295 51 46 Dumont d'Urville 12 0.090 0.667 0.438 0.445 0.132 55 48 Esperanza 3 0.100 0.707 0.656 0.583 0.505 55 51 Faraday 1 0.100 0.741 0.721 0.589 0.555 55 54 Halley 6 0.100 0.646 0.454 0.429 0.184 55 51 Marambio 4 0.100 0.703 0.608 0.570 0.480 41 52 Marsh/ O'Higgins 3 0.100 0.681 0.608 0.637 0.557 47 52 Mawson 2 0.300 0.616 0.557 0.370 0.291 55 46 McMurdo/ Scott base 5 0.200 0.704 0.626 0.487 0.368 55 49 Mirny 6 0.100 0.635 0.534 0.445 0.285 55 48 Novolazarevskaya 2 0.200 0.581 0.505 0.332 0.250 51 46 Rothera 1 0.100 0.588 0.531 0.435 0.375 52 54 Syowa 2 0.300 0.576 0.517 0.325 0.260 50 46 Vostok 4 0.170 0.518 0.349 0.258 0.104 52 50

Earlier (Calibrate period 1957-1986; Validation period 1987-2011) Predictand Predictors Stations #f Crit Corr Cal r Val r RE CE years years Amundsen-Scott 1 0.340 0.388 0.445 0.362 -0.149 30 29 Bellingshausen 3 0.100 0.883 0.874 0.666 0.666 28 30 Byrd 3 0.220 0.595 0.761 0.517 0.052 21 28 Casey 2 0.300 0.580 0.560 0.318 0.216 27 30 Davis 3 0.209 0.488 0.739 0.525 0.521 26 29 Dumont d'Urville 1 0.300 0.519 0.455 0.190 0.161 30 28 Esperanza 3 0.100 0.733 0.742 0.592 0.584 30 30 Faraday 1 0.120 0.837 0.805 0.562 0.561 30 30 Halley 1 0.403 0.326 0.597 0.327 0.239 30 29 Marambio 3 0.109 0.416 0.795 0.450 0.450 17 30 Marsh/ O'Higgins 4 0.009 0.691 0.769 0.493 0.493 23 30 Mawson 1 0.200 0.611 0.676 0.406 0.364 30 29 McMurdo/ Scott base 1 0.370 0.469 0.640 0.387 0.345 30 29 Mirny 3 0.130 0.709 0.679 0.246 0.243 30 29 Novolazarevskaya 1 0.300 0.349 0.722 0.455 0.400 26 29 Rothera 1 0.230 0.590 0.695 0.384 0.372 27 30 Syowa 1 0.400 0.551 0.697 0.484 0.367 25 29 Vostok 1 0.310 0.275 0.547 0.390 0.027 28 28

Late (Calibrate period 1982-2011; Validation period 1957-1981) Predictand Predictors Stations #f Crit Corr Cal r Val r RE CE years years Amundsen-Scott 1 0.470 0.467 0.220 0.035 -0.035 30 24 Bellingshausen 2 0.340 0.748 0.935 0.802 0.801 30 27 Byrd 3 0.300 0.672 0.611 0.429 -0.088 23 22 Casey 7 0.150 0.573 0.586 0.245 0.242 30 26 Davis 4 0.400 0.689 0.426 0.231 0.189 30 22 Dumont d'Urville 7 0.150 0.523 0.620 0.262 0.254 30 25 Esperanza 4 0.020 0.813 0.668 0.249 0.237 30 26 Faraday 1 0.340 0.738 0.851 0.608 0.600 30 29 Halley 4 0.150 0.703 0.581 0.062 0.059 30 27 Marambio 1 0.470 0.743 0.569 0.227 0.223 29 27 Marsh/ O'Higgins 2 0.150 0.711 0.865 0.744 0.736 29 27 Mawson 1 0.520 0.675 0.479 0.261 0.256 30 22 McMurdo/ Scott base 7 0.050 0.653 0.621 0.401 0.379 30 25 Mirny 2 0.473 0.597 0.585 0.398 0.238 30 24 Novolazarevskaya 2 0.310 0.677 0.355 0.085 0.076 30 22 Rothera 1 0.410 0.615 0.723 0.475 0.423 30 29 Syowa 1 0.510 0.672 0.460 0.217 0.192 30 22 Vostok 6 0.200 0.580 0.346 0.123 -0.064 29 27

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