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Causes of Southern Hemisphere Climate Variability in the Early 20Th

Causes of Southern Hemisphere Climate Variability in the Early 20Th

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Causes of Southern Hemisphere variability in the early

20th century

A Thesis Presented to the

Honors Tutorial College, Ohio University

In Partial Fulfillment of the Requirements for Graduation from the Honors

Tutorial College with the degree of Geography

By Charlotte Connolly

April 2020

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This thesis has been approved by The Honors Tutorial College and the Department of Geography

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Dr. Ryan Fogt Professor, Geography Thesis Adviser

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Dr. Edna Wangui Director of Studies, Geography

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Dr. Donal Skinner Dean, Honors Tutorial College

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TABLE OF CONTENTS

Page

ABSTRACT ……………………………………………………………………………....5

1.0 INTRODUCTION……………………………………………………………...……..6

2.0 LITERATURE REVIEW ………………………………………………………...…11

2.1 Southern Hemisphere Modes of Natural Variability ………………………..11

2.1.1 ENSO ………………………………………………………..…..11

2.1.2 SAM ……………………………………………………….…….14

2.1.3 PDO/IPO …………………………………………………...……17

2.1.4 PSA ………………………………………………………….…..18

2.1.5 ASL ……………………………………………………...………19

2.2 Climate Variability by Country ……………………………………………..20

2.2.1 …………………………………………………..…20

2.2.2 and …………………………………..….22

2.2.3 ……………………………………………….….23

2.2.4 ………………………………………………………..24

3.0 DATA AND METHODS ………………………………………………………..….26

3.1 Data ………………………………………………………………………….26

3.2 Methods …………………………………………………………………...…31

4.0 DISCUSSION …………………………………………………………………….....35

4.1 Southern Hemisphere Changes in SLP in the 20th Century …………………35

4.1.1 Observations and Reconstructions ……………………………...35 4

4.1.2 Reanalyses ……………………………………………………....43

4.2 Changes in Antarctica ……………………………………………………….56

4.2.1 Observations and Reconstruction …………………………….…56

4.2.2 Reanalyses ……………………………………………………....59

4.3 Drivers in Changes ………………………………………………………….61

4.3.1 Influence from SAM …………………………………………….61

4.3.2 Influence from ENSO …………………………………………...71

4.3.3 Influence from PDO ……………………………………………..79

5.0 SUMMARY …………………………………………………………………………88

REFERENCES ………………………………………………………………………….98

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ABSTRACT

The long-term natural climate variability of the high in the Southern

Hemisphere is not well understood. This issue can be attributed to the lack of climate data and the short temporal span of data available for this . While the lack of data across the Southern Hemisphere makes studying long term climate difficult, it is not entirely impossible. Reanalysis and reconstructions are another data source that can be used to increase the span of available data. While these data sets are not perfect, they are necessary for a better understanding of Southern Hemisphere climate.

Using these multiple data sources, this study found that the early twentieth century is characterized by a negative SLP trend across and a positive pressure

SLP trend in across the midlatitudes in all except SON, where there is possible influence from ENSO. This study also found that reanalyses have less skill in the early twentieth century compared to the late twentieth century, which stems from less available data assimilated into the reanalyses, or their inability to conserve mass in of large spatial data voids. There were also connections between the PDO and large climate fluctuations across the Southern Hemisphere in the 20th century, but this relationship needs to be investigated more to fully understand these teleconnections.

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Chapter 1: INTRODUCTION

Compared to much of the , climate variability across the

Southern Hemisphere since the start of the 20th century is currently not well understood.

The lack of data both spatially and temporally in the high southern latitudes is largely responsible for this. Observations across Antarctica started in 1957, but these stations were sparse. Satellite data did not become available until after 1979. Combined with the vast in the southern , sparse data records make studying long term interactions across the extratropical southern latitudes challenging. Furthermore, with short and sparse climate records, recent trends and anomalies cannot be analyzed in context of long-term patterns, making the patterns difficult to link to natural variability or anthropogenic influences.

While the lack of observational and satellite data across the Southern Hemisphere makes studying long term variability challenging, it is not impossible. Reanalyses and reconstructions increase the temporal and spatial resolution of data. While these datasets are not entirely accurate and have varying skills and weaknesses, they are necessary if any advances in the scientific understanding of the Southern Hemispheric long-term variability are to be made. Reanalyses rely on observed data to complete calculations and constrain the spatially continuous output, which is a concern when reanalyses resolve the

Southern Hemisphere, especially before the 1950s. As mentioned earlier, the high latitudes of the Southern Hemisphere have little data before 1950, making it possible that reanalyses are not representing the high latitudes of the Southern Hemisphere accurately. 7

Marshall (2003) concluded that first generation versions of atmospheric reanalyses do a poor job resolving early twentieth century variability in the high latitudes of the Southern

Hemisphere for this reason. Reanalyses are useful for research, but understanding weaknesses are important. Similarly, reconstructions are created using different methods that vary between each, meaning the strengths and weakness vary between each as well.

The methods used to create the reconstruction influence its strengths and weaknesses, but carefully selected methods allow reconstructions to possibly overcome weaknesses in reanalyses. Nonetheless, the utilization of reanalyses and reconstructions are required to understand the past climate of the Southern Hemisphere to overcome the limited observations, but neither represent the atmosphere perfectly, making it important to use more than one dataset for a more accurate representation.

An important characteristic of recent Southern Hemisphere climate is the downward pressure trend over Antarctica that started in the 1960s. This trend has been linked to ozone depletion (Sexton 2001; Thompson and Solomon 2002; Fogt et al. 2009).

This downward trend in Antarctica was accompanied by increase in pressure over the midlatitudes likely driven to conserve mass. This pattern is visible in reanalyses and observations during this period. It is unknown if this relationship between the high and middle latitudes is a robust feature of the Southern Hemisphere, and one that occurs in the earlier portions of the 20th century. The lack of data before the 1960s makes studying the possible link between the high and middle latitudes challenging. If this relationship is robust, an increase in pressure over Antarctica would be accompanied by decreasing pressure across the midlatitudes to conserve atmospheric mass. 8

In this study, pressure reconstructions across Antarctica are used to understand the

Southern Hemisphere during the early twentieth century (Fogt et al. 2016a,b; hereafter,

Fogt reconstruction). In the early twentieth century the Fogt reconstruction shows a positive pressure trend in Antarctica despite many reanalyses, which are used for research purposes, resolving a strong negative trend. If the pattern observed in the late twentieth century holds true, then based on the Fogt reconstruction, the middle latitudes should experience a negative pressure trend in order to conserve mass. Even though the dynamics of the Southern Hemisphere are well represented in reanalyses, without the data from high latitudes, there is very little information restricting the output. With few data points assimilated into the reanalysis, it resolve erroneous patterns and miss some features of Southern Hemisphere climate. The conflicting trends between the Fogt reconstruction and reanalyses must be better analyzed if pressure relationships across the

Southern Hemisphere are to be understood. If the Fogt reconstruction is correct and the early twentieth century does have a positive trend over Antarctica, then the midlatitudes would have an overall negative pressure trend, assuming that the pattern seen in the late twentieth century is robust.

An underlying component to these relationships is the potential drivers for the connections and the way these drivers influence pressure across the Southern

Hemisphere. Particularly, if the pressure oscillation between the high and middle latitudes is a robust feature of Southern Hemisphere climate variability, these trends are likely influenced or driven by the large scale climate modes. Some of these climate modes include the El Niño-Southern Oscillation (ENSO), the Southern Annular Mode

(SAM), the Pacific-South American mode (PSA), and the Pacific Decadal Oscillation 9

(PDO). These climate modes and few more are discussed in detail in Chapter 2.

Generally, these climate modes account for most of the variability and teleconnection within the Southern Hemisphere, though some teleconnections of these climate modes are not well studied and understood, especially throughout the full 20th century.

In order to obtain the most accurate picture of variability across the Southern

Hemisphere, multiple data types are utilized for this study. These include observations from the middle latitudes and Antarctic stations, multiple reanalyses, Antarctic pressure reconstructions, SAM index reconstructions, and the Southern Oscillation Index (SOI) which is used to monitor ENSO. The reanalysis included in the study are the National

Oceanic and Atmospheric Administration/Cooperative Institute for Research in

Environmental Sciences (NOAA-CIRES) Twentieth Century Reanalysis version 2c

(hereafter 20CRv2c, Compo et al. 2011), the NOAA-CIRES-DOE version 3 (20CRv3,

Slivinski et al. 2019), the European Centre for Medium Range Weather Forecasts including their 20th-century reanalysis (hereafter ERA-20C, Poli et al. 2016), and a coupled -atmosphere reanalysis of the 20th century (hereafter CERA-20C,

Laloyaux et al. 2018). The reconstructions include pressure reconstructions of high observation stations (Fogt et al. 2016a, b) and a SAM index reconstruction (Fogt et al. 2009; Jones et al. 2009). The specific sources of data and methods for organizing the data can be found in Chapter 3.

By using the Fogt reconstruction and the observed data from the midlatitudes, this study will work to better understand the historical climate variability of the Southern

Hemisphere throughout the 20th century. This study will also work to understand the teleconnections between the mid and high latitudes, as well as understanding how 10 reanalyses represent the atmosphere in the early twentieth century. This will allow for a better understanding of strengths and weaknesses for each reanalysis and how they perform when little data are available to constrain the output.

The specific research questions for this study are thus:

1. How does the Southern Hemisphere temperature and pressure change both

spatially and temporally in the early 20th century?

2. How are these changes in the Southern Hemisphere midlatitude climate related to

changes in Antarctic climate in the early 20th century, based on reconstructions

and reanalysis data?

3. What are possible mechanisms to explain these changes throughout the Southern

Hemisphere prior to 1950?

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Chapter 2: LITERATURE REVIEW

2.1 Southern Hemisphere Modes of Natural Climate Variability

2.1.1 ENSO

ENSO is characterized by anomalous surface temperatures (SSTs) in the

Eastern Equatorial Pacific but it can influence climate variability as far as the poles.

There are three phases of ENSO, these include its warm (El Niño), cold (La Niña), and neutral phase. A warm (cold) ENSO is characterized by anomalous warm (cold) SSTs in the central to eastern Equatorial Pacific with below (above) average pressure anomalies in the eastern Pacific and above (below) average pressure anomalies in the western

Pacific. These pressure fluctuations are important for determining the state of ENSO.

Pressure values recorded at the observation stations Darwin, Australia and (French

Polynesia) are used to calculate the SOI and determine the phase of ENSO. The SOI is a twice-normalized difference in surface pressure between the two stations. A warm ENSO event is characterized by a negative SOI because the surface pressure at Darwin is greater than the surface pressure at Tahiti. ENSO events form between and with the strongest SST anomalies from to (Trenberth 1997).

ENSO influences multiple atmospheric variables and can have regional to large scale impacts. For many places in the Southern Hemisphere, ENSO has impacts on rainfall, temperature, and pressure (Philippon et al. 2012; Pohl et al. 2018). ENSO influences the middle and high latitudes by energy transport from wave trains (L’Heureux and Thompson 2006; Liess et al. 2014). Wave trains are Rossby waves that propagate energy and mass from one place to another. Some studies indicate ENSO has a limited 12 influence on the Southern Hemisphere, but that changes in the Southern Hemisphere can drive the next ENSO, especially large fluctuations in the sea level pressure around

Australia (Jin and Kirtman 2009; Hamlington et al. 2015).

While the direct influences on the Southern Hemisphere from ENSO may be less significant than that of the Northern Hemisphere, ENSO has been found to have direct connections to other important climate modes. ENSO has been found to influence the

Southern Annular Mode (SAM; L’Heureux and Thompson 2006), Pacific-South

American mode (PSA; Turner 2004; Fogt and Bromwich 2006), and the

Low (ASL; Turner et al. 2013). These climate modes are covered in more detail in the following sections.

Many studies have shown that an in phase relationship between ENSO and the

PSA increase the teleconnection between the and poles (Yu et al. 2015). A positive phase of the PSA is related to the cold phase of ENSO (Mo 2000). The PSA can be broken into two different types, the PSA1 and the PSA2 (Karoly 1989; Mo 2000; Mo and Paegle 2001). Generally, ENSO has been found to drive the onset of the PSA1 pattern and set up a strong wave train that allows energy to be transferred from the tropics to the poles more efficiently (Karoly 1989). ENSO also influences the PSA2 pattern, but the relationship is weaker (Mo 2000). Therefore, ENSO influences the high latitudes of the Southern Hemisphere mainly through energy transport through the PSA1 phase. More detail about the difference between PSA1 and PSA2 mode is discussed in section 2.1.5.

The connections between the ENSO and SAM appear only in seasons where

ENSO forcing is particularly strong. ENSO tends to be most mature during austral

(SON) and (DJF; Trenberth 1997; Fogt and Bromwich 2006; L’Heureux and 13

Thompson 2006). It is unknown whether this coupling between the SAM and ENSO is an outcome of climate change or natural variability as this relationship is difficult to study across a large time scale (Fogt and Bromwich 2006) but is likely a naturally occurring because of the dynamics behind the process. As ENSO induces in the tropics, upper-level divergence occurs and drives changes in the SAM (Revell et al. 2001).

The state of ENSO can influence many characteristics of the ASL, a region of semi-permanent low pressure off the coast of (Raphael et al. 2006).

During ENSO cold events the ASL strengthens and increases temperature advection and pressure anomalies off the coast of West Antarctica (Turner et al. 2013). The state of

ENSO can also influence the location of the ASL. For example, during an ENSO cold event the ASL is found more northwest. The location of the ASL can influence temperature anomalies on the of Antarctica and changes in sea ice extent

(Karoly 1989, Liu et al. 2002, Turner 2004). Specific details on how the ASL influences variability across Antarctica can be found in section 2.1.7.

ENSO events can be further broken down into two different forms with different teleconnections. These are called the Eastern Pacific (EP) and Central Pacific (CP) (Yu and Kao 2007; Kao and Yu 2009). The EP form of ENSO impacts only the PSA (PSA1 and PSA2). The CP form of ENSO impacts the PSA1 pattern, and the SAM (Yu et al.

2015). In the mid 1990s there was a shift from mostly EP events to CP events (Yu et al.

2015). Before this shift, the relationship between the SAM, ENSO, and the PSA was random. After the shift, the CP ENSO events modulated the PSA and SAM (Yu et al.

2015). This study was only able to look at ENSO events back to 1950, therefore, the 14 amount of EP verse CP events for 1900 – 1950 period is unknown, and the relationship between the SAM and ENSO in the early twentieth century is unknown.

The study by Yu et al. (2015) was not the only study to show the importance of the interactions between the SAM, ENSO and the PSA. Ding et al. (2012) recognized an abrupt shift in the relationship between the SAM and ENSO. Yeo and Kim (2015) were able to in pinpoint the shift in ENSO events to 1997-1998. The study investigated the periods 1979-1998 and 1999-2012 representing before and after the shift in dominant climate mode. The study found that the first period was dominated by influences from

ENSO and the second period was dominated by the SAM. It is unclear what drives these decadal fluctuations, but it may be driven by shifts in the PDO. During the mid 1990s there was a phase shift in the state of the PDO (Mantua et al. 1997, Kayano and Andreoli

2007).

2.1.2 SAM

The SAM describes the strength of the and the zonal movement of the polar jet that encircles Antarctica; it is considered the most influential high latitude climate mode of the Southern Hemisphere. It is divided into a positive and negative state with the most active from to (spring). The positive phase of the SAM is characterized by low pressure anomalies over Antarctica and high-pressure anomalies in the midlatitudes. Coupling between the troposphere and stratosphere drive oscillations between the positive and negative phase (Marshall 2003). A positive phase has a stronger pressure gradient, a stronger jet, and a poleward shift of the and storm track (Thompson and Wallace 2000). The stronger jet stream around Antarctica traps the cold dense air over the continent creating anomalous cold air with the exception 15 of the where warming occurs (Thompson and Wallace 2000; Gillett et al. 2006). During the positive phase of the SAM the midlatitudes experience warm temperature anomalies. The negative phase of the SAM has a stronger meridional component of the winds as well as opposite signed anomalies over the and the midlatitudes compared to its positive phase. Stations farther north, equatorward of 40˚S, show little to no correlation to the SAM (L’Heureux and Thompson 2006).

The SAM may occur at high latitudes in the Southern Hemisphere, but it is not an isolated from outside influences. The SAM has been connected to fluctuations in ENSO and the PSA (Ding et al. 2012; Yu et al. 2015). The interaction between these three climate modes may occur simultaneously as PSA events can occur during an ENSO warm event and set up a wave train to transport energy from the tropics to the high latitudes (Ding et al. 2012). This interaction is not robust and has been found to fluctuate.

The strongest relationship between all three climate modes has been since the 1990s after the shift in ENSO type events and the shift in the PDO (Mantua et al. 1997, Kayano and

Andreoli 2007, Yu et al. 2015). Much like the long-term variability, relationships between climate modes or the types of ENSO events prior to 1950 are not well understood.

In contrast, the connections between modes of climate variability in the Southern

Hemisphere can be better understood in the late twentieth century because of the available data. Studies that looked at the interaction between the SAM and the midlatitudes found changing interactions within the 1950 – 2000 period (Fogt and

Bromwich 2006; Sen Gupta and England 2006; Silverstri and Vera 2009). A poleward migration of the anomaly centers located in middle latitudes is noticeable between 1958 – 16

1979 and 1983 – 2004, especially over South America and Australia (Silverstri and Vera

2009). This means that some places in the midlatitudes have been experiencing a decreasing relationship to the SAM in the late twentieth century. Of course, this relationship is not well studied in the early twentieth century.

In recent years the SAM has experienced a large positive trend in the summer and a small positive trend in the and fall (Marshall 2003; Ding et al. 2012; Cerrone and

Fusco 2018), while there is no significant change in the SAM index during SON

(Marshall 2003; Ding et al. 2012). This anomalous trend in the summer SAM index has been tied to stratospheric ozone depletion in the recent decades and not to natural variability (Sexton 2001; Polvani and Kushner 2002; Thompson and Solomon 2002).

Since the state of the SAM influences the location of the Southern Hemisphere storm track, this climate mode can also influence precipitation and SLP anomalies in the midlatitudes. A positive SAM drives a poleward shift of the storm track (Thompson and

Wallace 2000). The SAM has some significant correlations to temperature and precipitation anomalies over Australia, though these significant correlations tend to shift between positive and negative periodically and are therefore not robust (Silvestri and

Vera 2009). The positive phase favors dry conditions over South America and New

Zealand while the negative phase favors below average temperatures and dry conditions over Australia, with above average temperatures over South America and New Zealand

(Silvestri et al. 2003, Silvestri and Vera 2009, Pohl et al. 2018). The SAM also has apparent influence on pressure anomalies in some locations across the midlatitudes as it drives high pressure anomalies during its positive phase (Marshall 2003).

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2.1.3 PDO / IPO

The Pacific Decadal Oscillation (PDO) has a similar spatial signature to that of

ENSO but occurs on a 20-to-30-year time span instead of the 2-to-7 year interval of

ENSO. Since the PDO has the same spatial signature of ENSO, the state of the PDO can influence the strength of an ENSO and drive climate variability in some of the same regions as ENSO (Gershunov and Barnett 1998). It is predominantly phase shifts in the

PDO (i.e., from positive to negative PDO phases) that influence the southern latitudes.

The PDO has shifted phases four times during the twentieth century, 1925, 1947, 1977, and the mid 1990s (Zhang et al. 1997; Mantua et al. 1997; Minobe 1997, Kayano and

Andreoli 2007). These phase shifts can influence variability in the polar regions, as it has been shown that a shift to a negative PDO causes the ASL to deepen (Clem et al. 2015).

The PDO shifts between two phases, a warm and a cold. The PDO was is its warm phase from 1925-1946 and 1977-mid 1990s (Mantua et al. 1997; Mo and Paegle 2001, Kayano and Andreoli 2007).

Studies have indicated that the PDO can determine global teleconnections. The

PDO can allow for set up of a wave train that allows the tropics to more easily interact with the poles (Mo and Paegle 2001). Post 1970s there has been an increase in the relationship between the SAM and ENSO oscillation, this relationship decreases prior to the 1970s (Mo and Higgins 1998; Mo and Paegle 2001). This shift in the relationship between ENSO and SAM may be a product of the shift in the PDO. A positive PDO becomes positively correlated to the SAM and is related to a positive trend in the SAM

(Goodwin et al. 2016). 18

The Interdecadal Pacific Oscillation (IPO) is similar to the PDO but occurs on larger spatial scale (Dong et al. 2018). It is characterized by anomalous SSTs in Pacific

Ocean from the Southern Hemisphere across the Northern Hemisphere , whereas the PDO is more marked in the tropics and Northern Hemisphere only. The IPO and PDO have similar time scales, with phase shifts (between negative and positive) occurring every 20-to-30 years. During the twentieth century, there have been two warm

IPO events, 1924-1945 and 1977-1998 (Dong and Aigue 2015). The phase switches of the IPO have been connected to global changes in SST, temperature, and pressure (Dong and Aigue 2015, Dong et al. 2018). The global cooling experienced between the 1940s and the 1970s may have been driven by the shift of the PDO and IPO (Fang 2018).

2.1.4 PSA

The PSA drives anomalous pressure systems in both the Pacific and Atlantic

Ocean and related to ENSO. An ENSO warm event is associated with a high-pressure anomaly in the South Pacific, and a low-pressure anomaly in the South Atlantic. The pressure anomaly in the Pacific initiates a wave train connecting the tropics to the higher latitudes of the Southern Hemisphere. Convection in the tropics may initiate the formation of the PSA, explaining the strong ties to ENSO events (Mo and Higgins 1998).

The connection between the PSA and ENSO creates two major frequencies of the PSA.

The first is called PSA1 which is an oscillation of SSTs in the Pacific Ocean and is related to the frequency of ENSO (Karoly 1989). The second called the PSA2 is linked to the quasi-biennial oscillation component of ENSO events, which is the oscillation between westerly and easterly wind in the equatorial stratosphere. PSA2 events have an oscillation period of 22 – 28 months and occur during the spring season (Mo 2000). 19

Though both PSA1 and PSA2 are associated with ENSO, PSA1 is more likely to occur

(Mo and Paegle 2001). The positive (negative) phase of PSA1 is the response to warm

(cold) ENSO event. The PSA is most likely to occur due to the persistence of the SSTs in the tropics (Mo and Paegle 2001). The strong and persistent PSA1 climate mode acts to drive anomalies in the midlatitudes from ENSO (Mo and Paegle 2001; Mo and Higgins

1998). Both PSA1 and PSA2 are both associated with a wave 3 hemispheric pattern that aids in the set-up of the well-defined wave train from the tropical Pacific to the high latitudes (Mo and Paegle 2001). These wave trains are how PSA influences the climate from the tropics to Antarctica.

2.1.5 ASL

The ASL is a persistent low-pressure system off the coast of West Antarctica in the Pacific sector that exists between the Antarctic Peninsula and the Ross Ice Shelf. This persistent system moves based on the season and influences from other climate modes

(Fogt et al. 2012). For example, a positive SAM creates a stronger ASL that is located farther south than normal. Shifts in the location and strength of the ASL influence temperature and pressure anomalies over the Antarctic continent. This is because the

ASL influences the wind direction and temperature advection over the continent

(Hosking et al. 2013).

There is a well-defined annual cycle in the average zonal location of the ASL, with the low being found immediately west of the Antarctic Peninsula in austral summer

(DJF) and moving westward to the by winter (JJA; Fogt et al 2012; Turner et al.

2013). There is an annual cycle in the meridional location of the ASL, as the low is farther north in summer and at its most southerly location in late winter. In contrast, the 20 absolute depth of the ASL has a semiannual form, with the lowest pressure in the equinoctial seasons, since this is the dominant cycle observed in the MSLP fields in the

Antarctic coastal zone (Turner et al. 2013b). Hosking et al. (2013), which removed the influences from the SAM, have shown that the ASL has a seasonality to its strength. In the JJA the ASL is deeper, farther west, and characterized with stronger meridional winds. The DJF season has the opposite characteristics, the ASL is weaker, weaker winds, and located farther to the east. The location of the ASL and the strength of the winds can influence temperature, precipitation, and sea ice extent across Antarctica.

The ASL, though positioned near West Antarctica, can be influenced by ENSO in the tropics. The ASL is stronger during the La Niña phase because of the wave train in the Pacific which causes blocking in the South Pacific (Yuan and Martinson 2001;

Raphael et al. 2016). This relationship between ENSO and the ASL allows ENSO in the tropics to influence the poles.

Recently the ASL has been strengthening driving lower SLP across the South

Pacific (Clem et al 2015). These recent changes in the ASL have been attributed to natural variability and not to changes in the SAM, Antarctic stratospheric ozone concentrations, or greenhouse gases (Turner et al. 2016).

2.2: Climate Variability by Country

2.2.1

Studies that analyze the long-term climate variability of South Africa are sparse and the ones that do exist tend to focus on variability in rainfall. South Africa is characterized by exceptionally dry conditions in all seasons except for the austral 21 summer. This dramatic shift in precipitation is connected to the movement of a persistent high-pressure system driven by changes in pressure over the (Reason et al.

2002). The system over the Indian Ocean is called the South Indian Convergence Zone

(SICZ). Long-term precipitation variability is also influenced by ENSO, PDO, and SAM

(Pohl et al. 2018; Phillippon 2012). ENSO influences the SSTs of the Indian ocean which in turn affect the location and strength of the SICZ, which influences precipitation over

South Africa. Higher SST anomalies over the Indian Ocean create drier conditions over

South Africa as it increases the subsidence associated with the high pressure system.

The correlation between ENSO and South African rainfall is complicated and possibly nonexistent (Reason et al. 2002). The connection of ENSO to South Africa climate variability may be indirect and not robust. A later study completed by Blamey and Reason (2007) supported the non-robust correlation between South African precipitation and ENSO, stating that though a relationship between ENSO and South

Africa exists at times, the 10 wettest and driest from 1921 to 2004 are during neutral ENSO years. This study concludes that the climate variability of South Africa is likely influenced by a combination of climate modes such as ENSO and SAM due its location in three highly variable oceans (Atlantic, Indian, and Southern). Other studies have also concluded that precipitation across South Africa has variability on multiple scales. This includes oscillations on the order of 2-8 years and 15-28 years corresponding to ENSO and the PDO respectively (Pohl et al. 2018; Philippon et al. 2012; Reason et al.

2000). Therefore, it might not be possible to attribute just one climate mode to the variability of South Africa.

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2.2.2 Australia and New Zealand

In comparison to South Africa, many studies have worked to understand climate variability across Australia and New Zealand. During much of the twentieth century,

1920 to 1999, ENSO was found to drive most variability over Australia (Pui et al. 2012,

Risbey et al. 2009), with a warm event decreasing rainfall across Australia (Karoly 1989,

Risbey et al. 2009) and causing anomalous high pressure over Australia (Hamlington et al. 2015). The high pressure over Australia may act to drive the onset of the next ENSO event by causing equatorward winds (Hamlington et al. 2015). The influence from an

ENSO warm event may be strengthened during a positive Indian Ocean Dipole (IOD) event when SST anomalies are higher in the western Indian Ocean and lower northwest of Australia (Saji and Yamagata 2003). Other studies show that the relationship between

ENSO and Australian variability is not stable (Ashcroft and Karoly 2014). For example, the relationship between ENSO and Australian rainfall variability decreases in the early twentieth century. This decrease in relationship with ENSO is not unique to Australia but is a fact a global characteristic of early twentieth century climate variability (Suppiah

2004, Fogt and Bromwich 2006).

The SAM also influences variability across Australia and New Zealand by inducing pressure anomalies and changing the storm track. The SAM has a larger influence on New Zealand variability than Australian variability. The correlation between the SAM index and Australian variability has been shown to be unstable due to the fact it varies depending on the periods analyzed (Silvestri and Vera 2009). The correlation between the SAM and climate variability across New Zealand may also not be significant or robust, but no climate mode was found responsible for a significant amount of climate 23 variability across New Zealand (Godia et al. 2016), making the SAM the most important climate mode.

2.2.3 South America

South America has oceans on both sides and the steep Mountain range on the western border. Being surrounded by large bodies of water means South America is surrounded by high pressure systems over the oceans with low pressure over the continent called the Low. This pattern is responsible for seasonal variability across the continent as the SSTs effect the location and magnitude of the pressure systems (Paegle and Mo 2001).

Much of the variability across South America can be explained by oscillations in

ENSO (Kayano 2003; Vera et al. 2004). Unlike in Australia / , the relationship between South America and ENSO is more stable and significant when looking at ENSO influences of SLP, winds, and temperature across South America (Kayano et al. 2019).

Kayano and Andreoli (2007) found that the state of the PDO modulates the influence

ENSO has on South American climate variability. For example, when ENSO and the

PDO are in the same phase, the teleconnections between ENSO and South American variability increase. The opposite is true when ENSO and PDO are out of phase.

The variability across South America has also been linked to SAM (Silvestri et al.

2003), the PDO (Kayano and Andreoli 2004), and the PSA (Mo and Paegle 2001).

Influence from PDO is dependent on the season and phase of the PDO, with climate variability in the and months greater during a warm PDO than for a cold PDO (Kayano and Andreoli 2007). The PSA moderates rainfall by influencing pressure anomalies in the high latitudes of the South Pacific and South 24

(Mo and Paegle 2001). Silvestri and Vera (2003) found that a positive SAM has increased anticyclonic anomalies in the Pacific Ocean and decreased precipitation over

South America. This relationship may be newer and not robust as a study by Sen Gupta and England (2006) found that the strong relationship between the SAM and precipitation began around 1979, with much weaker correlations farther back in time. Further, Silvestri and Vera (2009) also found that when looking at larger time scales, SAM and South

American precipitation are independent of each other.

The recent relationship between the SAM and precipitation of South America may be associated with the strong connection between ENSO and SAM during the late twentieth century (Fogt and Bromwich 2006). This would indicate that the climate variability of South America is dependent on both the state of ENSO and the SAM, and explains why the correlation decreases farther back in time, as the relationship between

ENSO and SAM decreases in the early twentieth century.

2.2.4 Antarctica

Antarctica is a high elevation ice sheet surrounded by the circumpolar trough, a region of high cyclonic activity. Variability in the Antarctic circumpolar belt affects variability across Antarctica and some midlatitude areas. These patterns are not well understood in the long-term natural variability owed to the very little observational data.

Possibly the most direct and influential forcing of Antarctic variability is the SAM, as it deals with the strength of the jet stream circumventing the continent. The SAM has strong impacts on storm tracks and temperature advection across Antarctica. Another influence of Antarctic variability is the ASL, which also influences the winds and temperature advection over most of West Antarctica and the Antarctic Peninsula (Fogt et 25 al. 2012; Hosking et al. 2013; Turner et al. 2013). The lowest surface pressure occurs during late JJA and early SON when the continent also experiences its coldest temperatures.

In the 1960s ozone depletion caused a downward pressure trend over the continent (Karoly 1989; Thompson and Solomon 2002; Fogt et al. 2009). The implications of large pressure changes over Antarctica are not fully understood and the effects from ozone depletion on the observational data makes studying natural long-term variability challenging, as it is hard to differentiate between natural variability and anthropogenic influences. This is especially true since observations begin around the same time ozone loss started.

26

Chapter 3: DATA AND METHODS

3.1 Data

To address the research questions, the study will utilize multiple data types in an attempt to create a more complete picture of Southern Hemisphere historical climate variability. These data types include observations, reconstructions, and reanalyses. The data come from multiple sources and were prepared by different methods before utilized in this study.

The first set of data are observational measurements of SLP from the Southern

Hemisphere midlatitudes. These datasets were gathered from the Monthly Surface

Stations Climatology dataset on the National Climate Data Center. Depending on the when the station was installed, and updated afterwards, the stations had different starting points and percentage of completeness. To successfully analyze the Southern Hemisphere during the twentieth century, the stations had to start at or before 1900. Some stations such as Orcadas, Sarmiento, and started taking observations in 1903 but where still used in order to increase data resolution of the observation station dataset.

When stations had missing data because of malfunction or the station was moved, a patching method was used to fill in the gaps. The patch was accomplished by using another observation station which fulfilled three criteria. The new station had to be close to the original station, had to have data during the gap of the original station, and enough data overlapping the original data so calculations could be carried out. Finding stations that fulfilled these criteria ensured the values retrieved from this calculation represent the trend at the station accurately. For a few stations in South Africa, no observation stations 27 in the Monthly Surface Stations Climatology dataset fit the three criteria. To use these stations in South Africa, data from National Centers for Environmental Information

(NCEI) had to be used to complete the patch. Once a station that fits the three criteria is found, the patching calculations following equation 3.1.1 can be done to create a continuous dataset.

Equation 3.1.1

푒푛푑 푦푒푎푟 1 [ ( ∑ 푥 − 푥 ) ] + 푥 푁 표푟𝑔 푝푎푡 푝푎푡 푠푡푎푟푡 푦푒푎푟 푚표푛푡ℎ

During the periods of overlap, the monthly data for the original station (xorg) set was subtracted by the data from the station being used for patching (xpat). For each month these values were averaged by the number of months used (N) to return the average monthly difference between the stations. This difference was added to the nearby station values during the gap to fill in the missing points of the original station. In some cases, more than one station was required to complete a patch. The stations used in this study is found in Table 3.1. Information about each station and the stations that were used to patch the original station can be found in Table 3.1 as well.

Monthly observational pressure data from Antarctica were gathered from the

British Antarctic Survey. These stations can also be found in Table 3.1 as well as the location of the station and the years operational. These stations are denoted by a star (*) after the name of the station. A map showing the location of each observation station used in the study is found in Fig. 3.1. 28

Fig. 3.1 Location of Observation Stations. Blue represents Antarctic stations. Red represents midlatitude stations.

29

Table 3.1 Location of observation stations, years active, percentage of completeness, and station used to complete patching if needed.

Station Name Latitude Station ID Start Year End Year % Complete Patched Station ID STANLEY 51.0S 57.0W 999006 1900 1982 98.80 888900 GRYTVIKEN 54.0S 36.0W 889030 1905 2018 91.52 TAHITI 17.6S 149.6W 919380 1876 2018 100.00 ISLAS ORCADAS 60.7S 44.7W 889680 1903 2018 99.57 ST. HELENA IS. 16.0S 5.7W 619010 1892 2018 99.28 DURBAN 30.0S 31.0E 685880 1884 2018 99.01 NCEI 685880 34.0S 18.6E 688160 1841 2018 99.53 NCEI 688160 PORT ELIZABETH 34.0S 25.6E 688420 1887 2018 99.81 NCEI 688420 22.9S 43.2W 837430 1851 2018 99.36 837810 33.4S 70.8W 855740 1861 2018 99.74 VALDIVIA 39.6S 73.1W 857660 1899 2018 99.44 857430 PUNTA ARENAS 53.0S 70.8W 859340 1889 2018 99.68 CATAMARCA AERO 28.6S 65.8W 872220 1901 2018 98.73 873450 CORDOBA AERO 31.3S 64.2W 873440 1873 2018 99.77 34.6S 58.5W 875850 1858 2018 99.02 875760 BAHIA BLANCA AER 38.7S 62.2W 877500 1896 2018 97.97 SARMIENTO ARGENT 45.6S 69.1W 878490 1903 2018 98.78 878600 AERODRO 37.0S 174.8E 931190 1863 2018 77.44 933090 41.3S 174.8E 934340 1864 2018 99.35 934170/936780 HOKITIKA AERODRO 42.7 S 171.0E 936150 1866 2018 91.34 936140 CHRISTCHURCH 43.5S 172.5E 937800 1864 2018 98.66 937810 DUNEDIN MUSSELBU 45.9S 170.5E 938940 1864 2018 99.14 5397 CHATHAM ISLAND 44.0S 176.6W 939870 1878 2018 98.82 945760 ALICE SPRINGS 23.8S 133.9E 943260 1885 2018 99.56 BRISBANE 27.4S 153.1E 945780 1887 2018 99.94 PERTH AIRPORT 31.9S 116.0E 946100 1876 2018 99.83 ADELAIDE AP AUST 35.0S 138.5E 946720 1857 2018 99.69 REGIONAL 33.9S 151.2E 947680 1859 2018 99.32 947670 MELBOURNE 37.8S 145.0E 948680 1903 2018 99.93 948650 HOBART AIRPORT 42.8S 147.5E 949750 1866 2018 96.46 AMUNDSEN-SCOTT * 90.0S 0.0E 890090 1957 2018 100.00 BELLINGSHAUSEN * 62.2S 58.9W 890500 1968 2018 99.86 BYRD * 80.0S 119.4W 893240 1957 2011 85.62 CASEY * 66.3S 110.6E 896110 1960 2018 98.79 DAVIS * 68.6S 78.0E 895710 1957 2018 92.34 DUMONT D'UVILLE * 66.7S 140.0E 896420 1956 2018 95.63 ESPERANZA * 63.4S 57.0W 889630 1945 2018 95.37 FARADAY * 65.3S 64.3W 890630 1950 2018 95.02 HAILEY * 75.5S 26.7W 890220 1956 2018 98.54 MARAMBIO * 64.2S 56.7W 890550 1970 2018 98.13 MARSH / O'HIGGINS * 62.2S 58.9W 890560 1963 2018 95.24 MAWSON * 67.6S 62.9E 895640 1954 2018 99.49 MCMURDO / SCOTT BASE* 77.9S 166.7E 896640 1956 2012 97.75 MIRMY * 66.6S 93.0E 895920 1956 2018 99.60 NOVOLAZAREVSKAYA * 70.8S 11.8E 895120 1961 2018 99.43 ROTHERA * 67.6S 68.1W 890620 1946 2018 91.21

30

Observational data from Antarctica cannot be used to look at variability prior to

1950, therefore, station reconstructions are used instead. For this study, the Fogt reconstruction (Fogt et al. 2016a,b) will be utilized for Antarctic station data prior to

1950. When creating this reconstruction, it is assumed that the interactions between the midlatitudes and high latitudes remain stationary throughout the entire reconstruction period. This was done by using observational data from midlatitude stations and a statistical model based on linear regression to calculate and reconstruct the observational datasets of Antarctic stations back to 1905. While these data only extend back to 1905, it will still be utilized to look at the period between 1900-1950. More detailed information on this reconstruction can be found in Fogt et al. (2016a; 2016b). Another reconstruction used in this project is a SAM index reconstruction (Fogt et al. 2009; Jones et al. 2009).

This reconstruction extends the SAM index back to 1900, allowing this dataset to be used for both periods in the study. Southern Oscillation Index (SOI) data were gathered from the Australian Bureau of Meteorology

(http://www.bom.gov.au/climate/current/soi2.shtml) and represents the state of ENSO as it describes pressure fluctuations around the epicenter of ENSO activity. The NCEI PDO index was gathered from NOAA and used to represent the state of the PDO

(https://www.ncdc.noaa.gov/teleconnections/pdo/).

Four reanalyses were utilized in this study in order to understand how well each of the reanalyses resolve the Southern Hemisphere in the early twentieth century. The four reanalyses used were from NOAA-CIRES 20CRv2c (Compo et al. 2011), the

NOAA-CIRES-DOE 20CRv3, (Slivinski et al. 2019), the ERA-20C (Poli et al. 2016), 31 and the CERA-20C which is a coupled ocean-atmosphere reanalysis (Laloyaux et al.

2018).

All of these data sources are important when it comes to understanding the teleconnections of the Southern Hemisphere during the twentieth century as well as interactions between the high and mid latitudes. By using this diverse set of data, this study can analyze patterns during the twentieth century of the Southern Hemisphere.

3.2 Methods

In order to understand variability of the Southern Hemisphere in the early and late twentieth century, two focus periods, 1900-1950 and 1950-2000, are utilized. The 1900-

1950 is characterized by very little observational data and less understanding of the variability and teleconnections. The 1950-2000 is characterized by more observational data and a better understanding of teleconnections and variability, particularly over

Antarctica. These focus periods were selected for two reasons. First, they allow for analysis and comparison of the Southern Hemisphere in terms of the early twentieth century and the late twentieth century. Secondly, they allow for an understanding of how reanalyses resolve the early twentieth century where there is little observational data available to constrain the output.

Equation 3.2.1

푦 = 푚푥 + 푏

Equation 3.2.2 ∑(푥 − 푥̅) ∗ (푦 − 푦̅) 푚 = ∑(푥 − 푥̅)2 32

To analyze change, this study relies heavily on linear regression to represent rate of change. To do this, equation 3.2.1 needs to be solved for the slope (m). This is done from equation 3.2.2 using the years (x) and the corresponding SLP (y).

When looking at reanalyses, the trend at each coordinate point was calculated for our focus periods using the equations 3.2.1 and 3.2.2. For individual stations, the trend from each station was calculated across the same periods, except for the Antarctic stations in the early twentieth century period. For these stations the trend was calculated from 1905-1950 because of the data available. These values are especially useful when comparing the early and late twentieth century, since both focus periods use the same temporal size (50 years), these values can be easily compared between each other.

Equation 3.2.3

(푏) 푡푛−2 ≈ 푆푏

Equation 3.2.4

2√푛 − 1 푠푦√1 − 푟 푛 − 2 푠푏 = 푆푥√푛 − 1

Significance of these trends are calculated using the t-statistic / distribution which is shown in equation 3.2.3 resulting in a two tailed probability that the hypothesized slope is zero. To calculate the t value the slope (b) of the line and the standard error of the regression line (sb) is needed. The standard error can be calculated by equation 3.2.4 where Sy is the standard deviation of SLP, Sx is the standard deviation of time, and n is the number of values. 33

In some cases, in order to understand significant patterns in the trends, anomalies

(푥푎푛표푚; equation 3.2.5) are calculated using the average of all the values (푥̅) subtracted from the individual values (푥). Anomalies thus represent the deviation from the average at a specific point.

Equation 3.2.5

푥푎푛표푚 = 푥 − 푥̅

Anomalies averaged over a range of events (either years, or grouped by a climate pattern like ENSO) can be tested for statistical significance using the Student’s t-test.

This test assumes the pressure anomalies are approximately normally distributed, so that tests on the sample average and standard deviation of the group averaged can be compared to the population mean (which is zero based on Equation 3.2.5). The equation for the t-statistic for a sample mean is given in Equation 3.2.6, with n-1 degrees of freedom for averages of a single group compared to the hypothesized population mean mu.

Equation 3.2.6

푋̅ − 휇 푡 = 푛−1 푆 푥⁄ √푛

34

To better understand strength of connections across the twentieth century running correlations (equation 3.2.7) are used. This returns the strength of a correlation with a value from negative one to positive one, positive one being highly correlated and negative one being not correlated between two data sets (x; y) of the same size (n).

Equation 3.2.7

푛 ∑푛(푥)(푦) − ∑푛 푥 ∑푛 푦 푟 = 𝑖 𝑖 𝑖 푛 2 푛 2 푛 2 푛 2 √[푛 ∑𝑖 푥 − (∑𝑖 푥) ][푛 ∑𝑖 푦 − (∑𝑖 푦) ]

35

Chapter 4: DISCUSSION

This research set out to answer multiple questions: How does Southern

Hemisphere pressure change both spatially and temporally in the early 20th century?

How are these changes in the SH midlatitude climate related to changes in Antarctic climate in the early 20th century? What are possible mechanisms to explain these changes throughout the Southern Hemisphere prior to 1950? The overarching goal behind these questions is to better characterize climate variability across the extratropical

Southern Hemisphere in the early twentieth century. Through the use of multiple datasets and multiple statistical methods, each of these questions have been investigated in the following sections.

Section 4.1 Southern Hemisphere Changes in SLP in the 20th Century

4.1.1 Observations and Reconstructions

Observational data are direct measurements that represent true trends at specific locations. While this data type can be spatially lacking in the Southern Hemisphere, it is the most accurate representation of the atmosphere. During the late twentieth century, observations from the mid latitudes and Antarctica are used to understand the relationship between these two regions. Table 4.1 shows the SLP trends and 95% confidence intervals at east station with a star after the station name denoting an Antarctic station. SLP is decreasing over Antarctica and increasing in the midlatitudes during DJF, MAM, and

JJA. This pattern is the weakest in SON with more stations recording trends of the 36 opposite sign.. MAM has the most statistically significant (p<0.05) SLP trends, particularly across New Zealand and Australia, while the other seasons have similar amount of significant SLP trends, mostly in South America.

37

Table 4.1 SLP trend per decade for each observation season and the 95% confidence intervals for 1950-2000. Stars after Station Name indicate an Antarctic station. Red values represent a positive trend, a blue value represents a negative trend. Values with grey background are significantly different from zero at p<0.05.

50-year 50-year 50-year 50-year Station Name Latitude Longitude +/- +/- +/- +/- Trend DJF Trend MAM Trend JJA Trend SON TAHITI 17.6S 149.6W 0.002 0.205 -0.001 0.128 0.032 0.139 0.004 0.144 ISLAS ORCADAS 60.7S 44.7W 0.413 0.532 0.680 0.565 -0.216 0.574 -0.004 0.530 ST. HELENA IS. 16.0S 5.7W 0.298 0.150 0.221 0.133 0.329 0.126 0.311 0.150 DURBAN 30.0S 31.0E 0.081 0.157 0.086 0.153 0.032 0.204 0.053 0.162 CAPE TOWN 34.0S 18.6E 0.025 0.149 -0.013 0.127 0.032 0.180 -0.030 0.140 PORT ELIZABETH 34.0S 25.6E 0.068 0.144 0.059 0.130 0.000 0.190 -0.030 0.151 RIO DE JANEIRO 22.9S 43.2W 0.366 0.138 0.229 0.152 0.194 0.268 0.356 0.189 SANTIAGO 33.4S 70.8W -0.371 0.259 -0.090 0.151 0.222 0.162 -0.110 0.135 VALDIVIA 39.6S 73.1W 0.359 0.221 0.313 0.300 0.548 0.339 0.353 0.300 PUNTA ARENAS 53.0S 70.8W 0.017 0.556 0.399 0.478 0.321 0.544 0.310 0.472 CATAMARCA AERO 28.6S 65.8W 0.507 0.184 0.298 0.224 0.686 0.313 0.575 0.238 CORDOBA AERO 31.3S 64.2W 0.424 0.162 0.364 0.251 0.470 0.256 0.436 0.188 BUENOS AIRES 34.6S 58.5W 0.074 0.183 -0.101 0.206 0.010 0.272 0.019 0.191 BAHIA BLANCA AER 38.7S 62.2W 0.194 0.237 0.097 0.255 0.197 0.318 0.348 0.326 SARMIENTO ARGENT 45.6S 69.1W 0.097 0.277 0.284 0.420 0.204 0.437 0.009 0.296 AUCKLAND AERODRO 37.0S 174.8E 0.029 0.332 0.562 0.305 0.235 0.548 -0.479 0.492 WELLINGTON 41.3S 174.8E -0.069 0.385 0.561 0.353 0.049 0.609 -0.380 0.537 HOKITIKA AERODRO 42.7 S 171.0E -0.078 0.391 0.557 0.337 0.043 0.615 -0.444 0.542 CHRISTCHURCH 43.5S 172.5E -0.177 0.454 0.420 0.400 -0.137 0.604 -0.473 0.588 DUNEDIN MUSSELBU 45.9S 170.5E -0.092 0.455 0.572 0.415 -0.020 0.615 -0.317 0.608 CHATHAM ISLAND 44.0S 176.6W 0.144 0.457 0.563 0.529 0.233 0.674 -0.346 0.583 ALICE SPRINGS 23.8S 133.9E -0.248 0.261 0.233 0.255 0.080 0.262 -0.208 0.215 BRISBANE 27.4S 153.1E 0.168 0.261 0.438 0.214 0.351 0.337 0.077 0.283 PERTH AIRPORT 31.9S 116.0E 0.063 0.224 0.425 0.234 0.358 0.331 0.000 0.225 ADELAIDE AP AUST 35.0S 138.5E 0.163 0.221 0.804 0.305 0.496 0.487 0.226 0.341 SYDNEY REGIONAL 33.9S 151.2E 0.126 0.255 0.548 0.296 0.377 0.468 0.140 0.342 MELBOURNE 37.8S 145.0E -0.031 0.244 0.546 0.331 0.216 0.533 0.049 0.387 HOBART AIRPORT 42.8S 147.5E 0.250 0.354 0.931 0.436 0.413 0.591 0.406 0.542 AMUNDSEN-SCOTT * 90.0S 0.0E -0.274 0.664 -0.428 0.661 -0.018 0.743 0.150 0.700 BELLINGSHAUSEN * 62.2S 58.9W -0.349 0.691 1.059 0.687 -0.338 0.873 0.311 0.781 BYRD * 80.0S 119.4W -1.685 3.829 0.421 1.085 0.946 1.010 1.480 0.833 CASEY * 66.3S 110.6E -0.327 0.696 -0.445 0.718 -0.413 0.856 0.175 0.638 DAVIS * 68.6S 78.0E -0.409 0.738 -0.609 0.659 -0.911 0.796 -0.227 0.618 DUMONT D'UVILLE * 66.7S 140.0E -0.315 0.727 -0.551 0.727 -0.249 0.818 -0.319 0.608 ESPERANZA * 63.4S 57.0W -0.523 0.731 0.441 0.691 -0.582 0.866 0.029 0.842 FARADAY * 65.3S 64.3W -0.577 0.697 0.779 0.756 -0.619 0.859 0.279 0.781 HAILEY * 75.5S 26.7W -0.598 0.747 -0.391 0.672 -0.369 0.725 0.120 0.683 MARAMBIO * 64.2S 56.7W -1.104 1.181 0.425 0.940 -0.470 1.393 -0.048 1.373 MARSH / O'HIGGINS * 62.2S 58.9W -0.818 0.836 1.044 0.684 0.496 1.064 0.588 1.002 MAWSON * 67.6S 62.9E -0.567 0.666 -0.589 0.602 -0.585 0.688 -0.076 0.592 MCMURDO * 77.9S 166.7E -0.478 0.836 -0.934 0.749 -0.419 0.920 0.005 0.732 MIRMY * 66.6S 93.0E -0.945 0.666 -1.052 0.656 -1.177 0.827 -0.499 0.628 NOVOLAZAREVSKAYA * 70.8S 11.8E -1.042 0.853 -0.773 0.827 -0.564 0.841 0.014 0.790 ROTHERA * 67.6S 68.1W -0.921 0.794 0.520 0.879 -0.619 0.990 0.355 0.894 38

Table 4.2 SLP trend per decade for each observation season and the confidence intervals for 1900-1950 Red values represent a positive trend, a blue value represents a negative trend. Values with grey background are significant.

50-year 50-year 50-year 50-year Station Name Latitude Longitude +/- +/- +/- +/- Trend DJF Trend MAM Trend JJA Trend SON TAHITI 17.6S 149.6W 0.052 0.18 0.161 0.183 0.062 0.202 -0.058 0.186 ISLAS ORCADAS 60.7S 44.7W -0.046 0.653 0.471 0.677 -0.111 0.489 0.111 0.681 ST. HELENA IS. 16.0S 5.7W -0.161 0.162 -0.133 0.136 0.147 0.182 -0.095 0.12 DURBAN 30.0S 31.0E -0.018 0.19 -0.158 0.258 0.135 0.32 -0.029 0.239 CAPE TOWN 34.0S 18.6E 0.079 0.156 0.037 0.142 0.137 0.199 -0.02 0.114 PORT ELIZABETH 34.0S 25.6E 0.163 0.119 0.014 0.183 0.304 0.25 0.072 0.162 RIO DE JANEIRO 22.9S 43.2W -0.137 0.225 -0.011 0.233 0.088 0.205 0.197 0.182 SANTIAGO 33.4S 70.8W 0.093 0.122 -0.019 0.084 0.091 0.177 0.112 0.09 VALDIVIA 39.6S 73.1W -0.008 0.147 0.145 0.296 0.164 0.439 0.172 0.243 PUNTA ARENAS 53.0S 70.8W 0.222 0.425 0.444 0.612 0.37 0.655 0.243 0.619 CATAMARCA AERO 28.6S 65.8W -0.337 0.215 -0.455 0.263 -0.221 0.317 0.162 0.289 CORDOBA AERO 31.3S 64.2W -0.142 0.162 -0.015 0.217 -0.051 0.286 0.045 0.215 BUENOS AIRES 34.6S 58.5W -0.158 0.175 0.027 0.229 0.04 0.312 -0.018 0.224 BAHIA BLANCA 38.7S 62.2W -0.036 0.216 0.056 0.302 0.022 0.387 -0.011 0.311 AER SARMIENTO 45.6S 69.1W -0.034 0.259 -0.02 0.452 0.102 0.469 0.084 0.401 ARGENT AUCKLAND 37.0S 174.8E -0.072 0.434 0.107 0.48 -0.041 0.601 0.158 0.507 AERODRO WELLINGTON 41.3S 174.8E -0.376 0.542 -0.011 0.53 -0.154 0.683 0.13 0.658 HOKITIKA 42.7 S 171.0E -0.219 0.531 0.192 0.517 0.006 0.669 0.28 0.694 AERODRO CHRISTCHURCH 43.5S 172.5E -0.256 0.606 0.174 0.583 -0.043 0.667 0.384 0.691 DUNEDIN 45.9S 170.5E -0.374 0.709 0.033 0.608 -0.307 0.702 0.269 0.729 MUSSELBU CHATHAM ISLAND 44.0S 176.6W -0.195 0.721 0.534 0.644 -0.253 0.757 0.101 0.779 ALICE SPRINGS 23.8S 133.9E 0.008 0.217 0.194 0.186 0.306 0.278 0.228 0.217 BRISBANE 27.4S 153.1E -0.078 0.267 -0.04 0.254 -0.03 0.291 0.055 0.309 PERTH AIRPORT 31.9S 116.0E -0.119 0.22 -0.105 0.22 -0.011 0.361 0.043 0.247 ADELAIDE AP AUST 35.0S 138.5E -0.079 0.224 0.23 0.268 0.274 0.519 0.14 0.379 SYDNEY REGIONAL 33.9S 151.2E -0.303 0.285 -0.018 0.306 0.038 0.445 -0.043 0.394 MELBOURNE 37.8S 145.0E -0.332 0.252 0.041 0.326 0.064 0.575 -0.019 0.449 HOBART AIRPORT 42.8S 147.5E -0.202 0.425 0.485 0.435 0.295 0.688 0.315 0.644

Table 4.2 shows the observational data used for the early twentieth century. For

this focus period only midlatitude stations are used as there are no observations over

Antarctica. These trends indicate decreasing SLP across most of the midlatitudes during

DJF. During MAM, JJA, and SON, the SLP trend becomes largely variable between 39 observation stations. There are fewer significant trends during the 1900-1950 focus period compared to the late 20th century. Similar to the significant trends in the late twentieth centuries, the location of the significant trends also appears to be random.

Analyzing these trends at individual stations allows for understanding of changes in SLP at one station but grouping the stations by region allows these changes to be understood across a larger area. When all the stations within a region are averaged together, variability within each region becomes clearer. Table 4.3 investigates the SLP trends within the midlatitude regions in the early twentieth century in order to investigate the overall trend. During DJF the majority or regions are experiencing decreases SLP.

The only two areas not experiencing decreasing pressure is South Africa, which has near no change in SLP and Grytviken. The Station Grytviken in on the Island South Georgia and the Sandwich Islands are located a few hundred miles out from Antarctic Peninsula.

Again, MAM, JJA, and SON has a variety of SLP trends across the midlatitudes. Places such as South America and Australia have positive trends in all three non-summer seasons.

Table 4.3 SLP trend per decade averaged regionally during 1900-1950. Blue denotes a negative SLP trend and a red value denotes a positive SLP trend. Values with grey background are significantly different from zero at p<0.05.

Station DJF (+/-) MAM (+/-) JJA (+/-) SON (+/-) Tahiti -0.012 0.168 0.016 0.193 -0.027 0.187 0.062 0.182 Grytviken 0.055 0.619 0.385 0.639 -0.032 0.479 -0.067 0.677 St. Helena Is. -0.132 0.141 -0.088 0.119 0.196 0.162 -0.051 0.106 South Africa 0.003 0.120 0.000 0.151 0.156 0.203 -0.062 0.133 South America -0.092 0.138 0.044 0.197 0.151 0.233 0.157 0.196 New Zealand -0.122 0.478 0.153 0.458 -0.119 0.559 0.223 0.591 Australia -0.099 0.186 0.032 0.209 0.093 0.377 0.044 0.288 Total -0.251 0.162 -0.151 0.212 -0.146 0.194 -0.172 0.198

40

The SLP trends can be broken further for a clearer understanding of SLP variability. Figure 4.1 shows observation stations grouped by geographic region (to analyze spatial variations) and analyzed by a 30-year running trend (to analyze temporal variations) to show climate trends across the entire time span of 1900 - 2018. Values past the year 2000 are used in this calculation to increase the span of the 30-year moving trend. The moving trend is calculated from the beginning point and not the end or middle point, i.e. the trend value at 1930 represents the 30 year trend from 1930-1959.

The observations have an overall positive pressure trend in the midlatitudes after

1960, in agreement with the downward pressure trend over Antarctica at the time (Fig.

4.1). Before 1960, the pressure trend becomes more variable. A notable feature of the 30- year running trend is the large positive SLP trend across New Zealand between 1920 and

1950 during DJF (Fig. 4.1a), although the DJF trend throughout the 1900-1950 averaged across New Zealand is still negative. Also visible across New Zealand is the strong negative trend in SLP during the same years but during MAM (Fig. 4.1b). Since natural climate variability across New Zealand is strongly driven by the the SAM (Godia et al.

2016), it is likely the SAM index is driving this pattern. When the SAM index is positive,

New Zealand is characterized by high pressure anomalies, and when the SAM index is negative, New Zealand is characterized by low pressure anomalies.

In DJF, with the exception of Austrailia and New Zealand, there are negative trends during the early twentieth century (Fig. 4.1a). These negative trends across much of the midlatitude observations indicate SLP is decereasing in many locations to offset the increasing SLP across Antarctica indicated by the Fogt reconstruction. Indicating that 41 the high and midlatiudes continue to work together in order to maintain atmospheric balance in the early 20th century.

Fig. 4.1 A 30 year-running trend of SLP using midlatitude observation stations. Horizontal lines separate stations by region.

Since no observational data are available over Antarctica during the early twentieth century, reconstruction data are used instead. Reconstructions are limited in the spatial extent they represent, but are related to observations and therefore have high skill in resolving atmoshperic patterns at these locations. This makes using reconstructions to represent Antarctic SLP trends during the early tweneiteth century possible. Together, 42 observations and reconstructions are used to understand the variability across Antarctica during the entire twentieth century.

Table 4.4 shows all the Antarctic stations used from the Fogt Antarctic reconstruction. The shown trends are from 1900-1950 and reflect the trend experienced across the midlatitudes during DJF. As Fig. 4.1a shows a negative trend across much of the midlatitudes during DJF, Table 4.4 shows a positive SLP trend across Antarctica also during DJF.

Table 4.4 SLP trend per decade for each observation season for 1900-1950 Red values represent a positive trend, a blue value represents a negative trend. Values with grey background are significant.

Station Name Latitude Longitude DJF (+/-) MAM (+/-) JJA (+/-) SON (+/-) Amundsen-Scott -90 0 0.54 0.496 -0.001 0.461 0.404 0.565 -0.05 0.242 Bellingshausen -62.2 -58.9 0.101 0.469 0.395 0.548 0.031 0.529 0.09 0.579 Byrd -80 -119.4 0.134 0.576 0.318 0.444 -0.273 0.632 -0.089 0.561 Casey -66.3 110.6 0.281 0.614 -0.212 0.374 -0.133 0.577 -0.136 0.465 Davis -68.6 78 0.742 0.565 0.011 0.294 0.261 0.515 0.078 0.368 Dumont d'Urville -66.7 140 0.249 0.546 0.123 0.49 -0.306 0.823 0.147 0.368 Esperanza -63.4 -57 0.25 0.69 0.186 0.454 -0.151 0.628 -0.075 0.696 Faraday -65.3 -64.3 0.081 0.597 0.137 0.55 0.091 0.639 0.227 0.8 Halley -75.5 -27.6 0.421 0.591 -0.01 0.397 0.291 0.463 -0.186 0.453 Marambio -64.2 -56.7 0.243 0.461 0.14 0.411 0.02 0.464 0.07 0.683 Marsh / -62.2 -58.9 0.088 0.452 0.277 0.576 -0.101 0.479 -0.01 O'Higgins 0.394 Mawson -67.6 62.9 0.674 0.576 -0.066 0.293 0.016 0.519 -0.193 0.308 McMurdo -77.9 166.7 0.443 0.691 -0.233 0.54 -0.153 0.691 -0.393 0.551 Mirny -66.6 93 0.775 0.54 -0.543 0.428 -0.242 0.648 0.094 0.411 Novolazarevskaya -70.8 11.8 0.367 0.511 -0.266 0.622 -0.223 0.594 -0.143 0.462 Rothera -67.6 -68.1 0.492 0.605 0.183 0.513 0.065 0.632 0.203 0.718 Syowa -69 39.6 0.456 0.471 -0.435 0.591 0.105 0.531 -0.245 0.384 Vostok -78.5 106.9 0.422 0.537 0.51 0.556 0.389 0.648 0.465 0.305

43

Between observations and reconstructions, the early twentieth century is shown to have increasing SLP over Antarctica and decreasing SLP in the midlatitudes during DJF.

The opposite is true for the late twentieth century with decreasing SLP over Antarctica and increasing SLP in the midlatitudes. According to the reconstructions and observations, SLP trends vary more between station in the early twentieth century during

MAM, JJA, and SON.

4.1.2 Reanalyses

While observations and reconstructions give an accurate picture of specific regions, there are limitations to the data. Observations are spatially limited to the region where the measurement was taken, and reconstructions are limited by the methods used to create the dataset. Reanalyses, while not void of limitations, provides another data source with spatial continuity and different construction methods.

The pressure trends from the two focus periods are shown in Figs. 4.2 through

4.9. Figures 4.2 through 4.5 represents the first focus period, 1950-2000. This plot includes the four reanalyses, with midlatitude observations, and Antarctic observations plotted on top. Figures 4.6 through 4.9 represent the second focus period, 1900–1950.

These plots include the same reanalyses and midlatitude observations, but since it covers the period with no observational measurements over Antarctica, the Fogt Antarctic pressure reconstruction is used.

As seen in Figs. 4.2 through 4.5, the late twentieth century is characterized by a negative pressure trend over Antarctica with a positive trend across the midlatitudes. This is resolved by every reanalysis and is seen in all seasons. Ozone depletion was occurring during most of this period and is believed to be driving the negative pressure trend across 44

Antarctica in austral summer. In order for the atmosphere to conserve mass, the negative

SLP trend over Antarctica causes SLP to increase in the midlatitudes. There is large agreement between each of the reanalyses in resolving this pattern during every season during this focus period.

Some of the common features resolved by the reanalyses are not found in the observational trend. During JJA the reanalyses all resolve the largest northward extent of a significant negative trend with an area stretching up into the Atlantic sector over the

Antarctic Peninsula despite observations across Antarctic Peninsula indicating a positive trend (Fig. 4.5). While the majority of the resolved patterns are similar, like the significant positive SLP trend on either side of Africa in DJF (Fig. 4.2), there are some disagreements. In DJF, 20CRv2c resolved the strongest significant positive SLP trend on the west side of South America (Fig. 4.2a), while 20CRv3 has a weak positive trend (Fig.

4.2b). CERA-20C and ERA-20C both have a positive trend that is significant at p<0.05

(Fig 4.2c, d). There is no observational data in the oceans around South America, so the magnitude of the positive SLP trend cannot be determined.

The reanalyses across Antarctica and the midlatitudes match the observations in all seasons except for SON (Fig, 4.5) and over the Antarctic Peninsula during JJA (Fig

4.4). In both the mid and high latitudes, SON is the season where the reanalyses resolve a different pattern than what is measured and shown in the observations. Schneider et al.

(2012), which looked at links between the tropics and Antarctica after 1979, found the connection between the two regions is strongest during SON. This occurs by the interaction between ENSO and the PSA which is strongest together during SON (Jin and 45

Kirtman 2009). This may be a teleconnection not well represented in the reanalyses causing it to do poorly in that season.

The pattern resolved by the reanalyses in the late twentieth century was expected to be reversed in the early twentieth century. This was expected as observations and the

Fogt reconstruction show this pattern between the regions, especially in DJF. However, this relationship between the mid and high latitudes is not resolved in any of the four reanalyses. Not only do the reanalyses not resolve the pattern this study expected to find, the reanalyses show very little agreement among themselves. It is not unexpected that reanalyses have little agreement amongst each other during the early twentieth century. A study completed by Clark and Fogt (2019) indicates the reanalyses fail to conserve mass in the early twentieth century. This is likely the reason why reanalyses do a poor job at resolving this period. CERA-20C has a positive SLP trend across Antarctica during

MAM, JJA, SON (Fig. 4.3d, Fig. 4.4d, Fig. 4.5d), but not the negative SLP trend across the midlatitudes. In all four seasons ERA displays a negative SLP trend across

Antarctica, with this trend being significant at p<0.05 in DJF and SON (Fig. 4.2c Fig.

4.3c, Fig. 4.4c, Fig. 4.5c). The agreement that exists between the reanalyses in the late twentieth century does not exist in the early twentieth century. With less data available to constrain the solution and the reanalyses inability to conserve mass, the reanalyses do very poorly and produce some unrealistic trends such as ERA-20C resolving a negative

SLP across Antarctica in all seasons

Since reanalyses have little skill at resolving the early twentieth century, it is also more likely for the reanalyses to disagree with the observations during this time period,

The inconsistencies between the reanalyses themselves as well as with observations in the 46 early twentieth century in some cases are severe, leading to discrepancies when using these datasets for research to understand variability during this period. As mentioned above ERA-20C and CERA-20C have the strongest trends. Both ERA-20C and CERA-

20C have a significant SLP increase in the middle latitudes during MAM, JJA, and SON, no matter what the reanalyses demonstrate over Antarctica. In contrast, 20CRv2c and

20CRv3 display a more balanced state with very few large trends in any of the four seasons. Overall, 20CRv2c and 20CRv3 has no significant trends over Antarctica and few regional areas in the middle latitudes of significant positive trends. The inconsistencies between reanalyses indicate they have little skill when resolving the pressure variability across the high southern latitudes in the early twentieth century. Overall, reanalyses resolve erroneous trends and patterns, as well as different trends from the observational trends recorded during this focus period.

Fig. 4.2 The DJF SLP trend during the first focus period (1950-2000) for a 20Crv2c; b 20CRv3; c ERA-20C; d CERA- 20C; with stippling indicating trends significantly different from zero at p<0.05. Dots represent observation station colored be magnitude of observed trend. 47

Fig. 4.3 As in Fig. 6, but for MAM

Fig. 4.4 As in Fig. 6, but for JJA 48

Fig. 4.5 As in Fig. 6, but for SON

Fig 4.6 The DJF SLP trend during the second focus period (1900-1950) for a 20Crv2c; b 20CRv3; c ERA-20C; d CERA-20C; with stippling indicating trends significantly different from zero at p<0.05. Dots represent observation station colored be magnitude of observed trend. 49

Fig. 4.7 As in Fig. 2, but for MAM

Fig 4.8 As in Fig. 2, but for JJA 50

Fig. 4.9 As in Fig. 2 but for SON

Though the reanalyses do not agree among each other and likely do not accurately represent the Southern Hemisphere in the early twentieth century, these tools are still useful for understanding teleconnections that occur in the Southern Hemisphere. When using these instruments to understand variability across the high latitudes, these weaknesses of the reanalyses need to be understood and kept in consideration. Utilizing other data types will help to overcome these challenges and allow for a more complete picture. Despite many of the reanalyses shortcomings, the reanalyses likely have higher skill in the midlatitudes. A spatially weighted and averaged time series was created of the

SLP from 15˚S-60˚S to further investigate variability from 1900 – 2018 in the midlatitudes as well as the reanalyses skill across this region. In Fig. 4.10, there is an increase in midlatitude pressure during the late twentieth century occurring in largely in

DJF and slightly in MAM (Fig. 4.10a, b). A noticeable feature is the upward trend in the 51 early twentieth century in all reanalyses, largely in MAM, JJA, and SON (Fig. 4.10b, c, d). DJF has a decreasing trend during this time (Fig. 4.10a). Therefore, while the spatial reanalyses show no overall trend within the midlatitudes during the early twentieth century, the trend does appear in the area-weighted average, especially when including more subtropical data / regions. This pattern is also captured in the observations. Table

4.3 shows the SLP observations averaged across regions as well as the average across all midlatitude observations. South America and Australia agree with this pattern in all seasons, decreasing SLP during DJF and increasing SLP during MAM, JJA, SON. The other regions to not follow this exact pattern, but the average trend across the midlatitudes do indicate a negative trend in DJF and a positive trend in all other seasons. 52

Fig 4.10 Spatially averaged SLP from 15˚S-60˚S using the reanalyses 20CRv3 (red), 20CRv2c (green), ERA-20C (blue), and CERA-20C (orange) for each season a DJF, b MAM, c JJA, and d SON.

Table 4.5 Correlation of average midlatitude SLP from Fig. 4.10 resolved by each reanalyses during 1900-2000

DJF ERA-20C CERA-20C 20CRv2c JJA ERA-20C CERA-20C 20CRv2c CERA-20C 0.793 CERA-20C 0.687 20CRv2c 0.839 0.625 20CRv2c 0.762 0.346 20CRv3 0.771 0.767 0.787 20CRv3 0.768 0.625 0.768

MAM ERA-20C CERA-20C 20CRv2c SON ERA-20C CERA-20C 20CRv2c CERA-20C 0.749 CERA-20C 0.607 20CRv2c 0.812 0.481 20CRv2c 0.615 0.206 20CRv3 0.739 0.609 0.808 20CRv3 0.563 0.495 0.753

53

A noticeable feature of the spatially averaged SLP is the local maximum that occurs near 1920 and 1945. No matter what the spread of the reanalyses are, the local maximum occurs in all reanalyses and in all seasons. The 1945 local maximum was investigated further by analyzing the composite time mean anomalies from 1940 – 1950

(Figs. 4.11-4.14). The reanalyses have some disagreements on where the positive anomalies occur, but the most commonly resolved significant at p<0.05 positive anomaly is in the Atlantic sector in all seasons except SON. Both 20CRv2c and CERA-20C tend to have the greatest local maximum during DJF (Fig. 4.10a) as well as the strongest positive anomalies in the Atlantic sector (Figs. 4.11a, d). The location of the significant positive anomaly varies within the Atlantic region. Some reanalyses resolve the anomaly closer to South Africa while others resolve it closer to the Antarctic Peninsula. The observations do not support this anomaly. The magnitude and location of the anomalies in the Atlantic sector can be compared to the local maximum in Fig. 4.10. A stronger anomaly or an anomaly that is farther north has a larger local maximum in Fig. 4.10.

While this anomaly does relate to the local maximum in Fig. 4.10, this is likely a result of the reanalyses poor skill during this period. During DJF the Orcadas station has a negative anomaly, not a positive one, and a positive anomaly that is weaker than the anomaly resolved by the reanalyses in all other seasons. The reanalyses also fail to capture the weak positive anomalies over South Africa, the reanalyses tend to resolve negative anomalies in this region. The station Grytviken which is located in the Atlantic sector has negative SLP anomalies for all seasons except JJA. Therefore, this local maximum is likely not reliable in the Atlantic sector. 54

Table 4.5 shows the correlation between resolved midlatitude SLP across the various reanalyses. This table indicates how well the reanalyses agree among each other.

The lowest correlation value of .206 is between CERA-20C and 20CRv2c during SON.

Interestingly, all of SON has low correlation values compared to the other seasons. DJF has the highest correlation values except for the correlation value between 20CRv2c and

20CRv3. This shows that the reanalyses have the largest agreement during DJF and the lowest agreement in SON, as reflected in the timeseries in Fig. 4.10.

Fig. 4.11 Averaged SLP anomaly from 1940-1950 for a 20CRv2c, b 20CRv3, c ERA-20C, and d CERA-20C. Stippled areas represent anomalies significantly different from zero at p<0.05. Dots represent observation station colored be magnitude of observed anomaly. 55

Fig. 4.12 As in Fig. 13, but for MAM

Fig. 4.13 As in Fig. 13, but for JJA 56

Fig. 4.14 As in Fig. 13, but for SON

Section 4.2 Changes in Antarctica

4.2.1 Observations and Reconstructions

Antarctic observations can only be used to understand variability in the late twentieth century, therefore, in order to look at pressure variability in the high latitudes during the early twentieth century, the Fogt Antarctic reconstruction data are utilized. A

30-year running trend can allow for a better temporal understanding of how SLP changes.

The values used in Figs. 4.2-4.9 use only one value to represent the focus periods but the

30 year running trend allows for a clearer temporal understanding. The 30-year running trend shows only DJF has negative SLP trend that starts consistently around 1960 (Fig.

4.15a). While the 50 year trend during 1950-2000 in Fig. 4.2 only indicates the overall trend for 1950-2000 is negative, by knowing this trend starts around 1960 it can be more 57 confidently linked to ozone depletion. MAM also has a negative SLP trend, but its initiation is less consistent and occurs slightly after 1960, indicating a longer lag between decreasing SLP and ozone depletion (Fig. 4.15b). Figure 16 shows no obvious negative

SLP trend during SON (Fig. 4.15c, d). Even on a smaller scale, SON is the most variable season (Fig. 4.15d).

Fig. 4.15 30-year running SLP-trend for a DJF, b MAM, c JJA, and d SON using the Fogt reconstruction. Running trend is calculated using the year as the first year in the calculation.

Generally, trends across Antarctica are much more variable when broken down from two 50 year trends to a running 30-year trend. The running 30-year trend indicates that there is much more regional and natural variability influencing the climate of 58

Antarctica, especially before 1960. For example, between 1920 and 1940 many Antarctic stations have a positive SLP trend. This is most evident in MAM and JJA (Fig. 4.15b, c), and only slightly evident in SON (Fig. 16d). Possible drivers of this anomaly will be discussed in section 4.3.

59

4.2.2 Reanalyses

While this study has already discussed reanalyses have poor skill at resolving

Antarctic pressure variability in the early twentieth century, reanalyses have nonetheless been used in many cases to investigate climate variability during the 20th century across

Antarctica. Therefore, understanding why reanalyses resolve the patterns they do is important. A spatially weighted and averaged time series of SLP can indicate how well reanalyses agree with the overall Antarctic SLP trend. While the reanalyses may resolve some things correctly, there are also some obvious issues. Based on Fig. 4.16, there are obvious disagreements among the reanalyses when resolving SLP over Antarctica. As expected, the SLP over Antarctica is lower in the latter half of the century and higher in the early half of the century. Generally, CERA-20C has a lower average SLP than the other three reanalyses.

A noticeable feature of Fig. 4.16 is how the spread between each reanalyses changes between the first and second focus period. During the late twentieth century there tends to be much more agreement on the magnitude and change of SLP. That is not the case for the early part of the century. The spread between each reanalyses increases and they start to disagree on the trends (Fig. 4.16). Tables 4.6 and 4.7 contain correlations of average Antarctic SLP resolved by each reanalyses across the first focus and second period. There is a vast difference between the correlation values between these two periods. This increase in spread between the reanalyses in the early twentieth century compared to the late twentieth century indicates an overall increase of uncertainty, which is supported by the correlation values during this period. 60

Fig. 4.16 Spatially averaged SLP from 60˚S-90˚S using the reanalyses 20CRv3 (red), 20CRv2c (green), ERA-20C (blue), and CERA-20C (orange) for each season a DJF, b MAM, c JJA, and d SON.

Table 4.6 Correlations of average Antarctica SLP from Fig. 4.16 resolved by each reanalyses during 1950-2000

DJF ERA-20C CERA-20C 20CRv2c MAM ERA-20C CERA-20C 20CRv2c 0.980 0.947 CERA-20C CERA-20C 0.951 0.961 0.931 0.955 20CRv2c 20CRv2c 20CRv3 0.973 0.982 0.715 20CRv3 0.941 0.967 0.598

JJA ERA-20C CERA-20C 20CRv2c SON ERA-20C CERA-20C 20CRv2c 0.957 0.908 CERA-20C CERA-20C 20CRv2c 0.942 0.931 20CRv2c 0.898 0.918 20CRv3 0.947 0.951 0.845 20CRv3 0.891 0.937 0.715

61

Table 4.7 Correlation of average Antarctic SLP from Fig. 4.16 resolved by each reanalyses during 1900-1950

DJF ERA-20C CERA-20C 20CRv2c MAM ERA-20C CERA-20C 20CRv2c CERA-20C 0.629 CERA-20C 0.401 20CRv2c 0.578 0.657 20CRv2c 0.548 0.577 20CRv3 0.383 0.486 0.715 20CRv3 0.411 0.427 0.598

JJA ERA-20C CERA-20C 20CRv2c SON ERA-20C CERA-20C 20CRv2c CERA-20C 0.238 CERA-20C -0.018 20CRv2c 0.364 0.467 20CRv2c 0.215 0.477 20CRv3 0.488 0.398 0.845 20CRv3 0.257 0.486 0.715

The reanalyses may have very little skill with the high latitudes, but there are some signals the reanalyses do resolve. For example, in Fig. 4.16, when looking at the midlatitudes, there is a spike in SLP around 1920 and 1940. This anomaly is seen in some of the reanalyses when averaged across Antarctica. This is likely an outcome of more available data to constrain the reanalyses as there were field campaigns during the 1910s and 1930s which have been shown to influence the reanalyses (Schneider and Fogt 2018).

Section 4.3 Drivers in Changes

4.3.1 Influence from SAM

Climate modes are large patterns of atmospheric variability and play a large role in driving trends in recorded climate variables. The correlation between climate mode indices and station data are used to understand the pressure variability associated with each climate mode. Correlations between the SAM index and midlatitude observation stations are plotted in Fig. 4.17. By investigating these correlations with a 30-year running correlation, this study hopes to understand how these relationships have changed over time. 62

Fig. 4.17 Running correlation between observational data from midlatitude stations and the SAM index for a DJF, b MAM, c JJA, and d SON

The SAM has many influences across the Southern Hemisphere. Some places are consistently influenced by the state of the SAM while other places have a relationship that fluctuates (Fig. 4.17). New Zealand has a consistent and positive correlation to the

SAM index. This is not unexpected as the New Zealand was highly weighted in the creation of one SAM index reconstruction and has variability largely influenced by the

SAM (Fogt et al. 2006). Specific stations in Australia are consistently correlated to the

SAM, while other regions are only consistently correlated in JJA and fluctuate in other seasons. Some countries have a more complicated relationship with the SAM. South 63

America, for instance, is positively correlated during DJF and MAM (Fig. 4.17a, b), negatively correlated during JJA (Fig. 4.17c), and has a fluctuating correlation during

SON (Fig. 4.17d). From about 1920 to 1940 and after 1970 to current, South America has been positively correlated to the SAM, and negatively correlated during the other times.

These time periods match the fluctuations of the PDO. The influence from the PDO may explain why some studies indicate SAM and ENSO are less correlated in the past (Fogt and Bromwich 2006; Sen Gupta and England 2006; Silverstri and Vera 2009).

In Fig 2 there was a noticable increase in SLP around 1930 in New Zealand. Since the SAM is strongly related to variability across New Zealand, the SAM index can be investigated to understand the anomaly. The time between 1930 and 1970 was selected to include all the values included in the SLP 30 year running trend (Fig 4.18). In this plot,

DJF has a large positive change in the SAM index with an increase of 0.21 hPa from

1930 to 1970 (Fig. 4.18a), although statistically insignificant. Since the SAM is trending toward a more positive state it might also drive the high pressure anomalies over New

Zealand. Continuing on this idea, MAM has the largest negative value of -0.48 (Fig

4.18b), significant at p<0.05. Since the SAM is trending toward a weaker state, it may relate to the decreasing SLP trend across New Zealand in MAM. 64

Fig. 4.18 The Sam index from 1930 to 1970 with trends calculated across the same period in the bottom left.

Overall, the SAM tends to influence Antarctic climate more than midlatitudes.

This is evident in Fig. 4.19. All stations during DJF, SON, and JJA are negatively correlated during all times (Fig 4.19a, c, d). This is because as the SAM enters its positive stage and increases in westerlies, it causes SLP to decrease across the continent. Notably, the correlation between the SAM index and Antarctic stations are not consistent during

MAM, some stations along the Antarctic Peninsula even have a negative correlation with the SAM index (Fig. 4.19b), likely tied to the seasonal position of the jet and the fact that the Peninsula is the farthest northern extent of Antarctica. In DJF there is a slight shift in the magnitude of the negative correlation around 1960 (Fig 4.19a).

65

Fig. 4.19 As in Fig. 19, but with Antarctic reconstructions and the SAM index.

The climate mode indices can also be compared to the reanalyses. The correlation between the SAM and reanalyses are plotted to help understand how specific regions are correlated to the SAM, and to further compare these correlations from the early twentieth century and the late twentieth century. These spatial correlations can be used to understand the spatial extent SAM has on the Southern Hemisphere. The late twentieth century is characterized by a stronger zonal gradient with the highest correlation values are between 30˚S and 60˚S and the lowest (most negative) correlation values are poleward of 60˚S. This was expected since a positive SAM decreases SLP over

Antarctica and increases SLP in the midlatitudes. In between the shift in sign of 66 correlation, there is a sharp gradient situated at 60˚S. DJF has the largest shift in correlations and the tightest correlation gradient.

The correlation between the SAM index and reanalyses looks very different in the early twentieth century. The reanalyses still have a negative correlation across Antarctica and a positive correlation across the middle latitudes, excluding specific reanalyses during specific seasons such as MAM for CERA-20C and DJF for ERA-20C. The difference between correlations in the mid and high latitudes is weaker in the early twentieth century, meaning the gradient around 60˚S is notably weaker or in some cases nonexistent. This is likely a result of reanalyses inability to accurately resolve natural variability in the early twentieth century.

These correlations indicate that the early twentieth century is less influenced by the SAM, but this could this also be an outcome of less Antarctic data to constrain the output; either of these hypotheses could explain the correlations of weaker magnitudes. In

Fig 4.19 there was no large shift in the correlation between the SAM and local SLP. This indicates that the SAM does not have a smaller influence in the early twentieth century, but instead the reanalyses struggle to accurately resolve these patterns.

67

Fig. 4.20 Spatial correlation between the SAM index and SLP for DJF during second focus period (1900-1950) for a 20CRv2c, b 20CRv3, c ERA-20C, and d CERA-20C

Fig. 4.21 As in Fig. 21 but for MAM

68

Fig. 4.22 As in Fig. 21 but for JJA

Fig. 4.23 As in Fig. 21 but for SON

69

Fig. 4.24 Spatial correlation between the SAM index and SLP for DJF during first focus period (1950-2000) for a 20CRv2c, b 20CRv3, c ERA-20C, and d CERA-20C

Fig. 4.25 As in Fig. 25 but for MAM 70

Fig. 4.26 As in Fig 25, but for JJA

Fig. 4.27 As in Fig 25, but for SON 71

4.3.2 Influence from ENSO

Another important driver of large-scale variability is ENSO, which occurs in the tropics and can be represented by the SOI value. Correlation between the SOI and the stations, similar to that done between the stations and the SAM index, is carried out in order to understand the teleconnection between ENSO and the midlatitude stations.

The correlation between SLP recorded across the midlatitudes and the SOI is displayed in Fig. 4.28, grouped by region to allow for spatial understanding of the correlation values. A few important and consistent patterns emerge. Australia has a predominantly negatively correlation with the SOI. A positive SOI, or ENSO cold event will drive low pressure anomalies across Australia. Also, the bottom station on the plot always has a strong negative correlation (Fig. 4.28). This is expected since the station is located on the island of Tahiti which is used in the SOI calculation. Other regions experience a seasonal relationship to ENSO. For example, South America has a mostly positive correlation to the SOI during MAM, SON, and JJA (except for a period in MAM between 1920 and 1940; Fig. 4.28b, c, d), but an overall negative correlation during DJF

(Fig. 4.28a). New Zealand has an overall positive correlation in all seasons except JJA where it is just slightly negative (Fig. 4.28a, b, d ).

72

Fig. 4.28 As in Fig. 19, but with correlation of observational midlatitude stations with the SOI.

To investigate how ENSO variability reaches the high latitudes of Antarctica, the station reconstructions are used. When looking at correlation between SLP across

Antarctica and the SOI, the interactions get slightly more complicated than influences in the SH midlatitudes (Fig. 4.29). During DJF there are fluctuations in the relationship between SLP and ENSO. Around 1910 the correlation between ENSO and Antarctic stations switches from negative to positive. This shift does not occur immediately, some stations have a greater lag time than others. Then, around 1950, the correlation switches from positive to negative at a similar pace (Fig. 4.29a). In summary, the transitions between ENSO and Antarctic SLP during DJF occur near 1910 and 1950. The drivers in this change is unknown, but influences teleconnections between the tropics and the poles. 73

Fig. 4.29 As in Fig. 19, but with the correlation of the Antarctic reconstruction and the SOI.

The other three seasons do not have as clear of a relationship to ENSO. Based on previous discussions, SON was expected to have the largest correlation or relationship to

ENSO, but the 30-year running correlation is weaker than DJF (Fig. 4.29d), although correlations remain fairly strong and positive across the Antarctic Peninsula, where the

ENSO relationship is strongest in SON (Clem and Fogt 2013). The overall weaker ENSO correlations with Antarctic station reconstruction could be a teleconnection that was lost in the creation of the Antarctic station reconstructions especially since SON has the lowest skill within the reconstructions. There is a period during SON from 1950 to 1980 where correlation values are larger than 0.5 (Fig 4.29d). There are also a few stations 74 during JJA that are consistently correlated to ENSO that may reflect the influence of

ENSO in late winter and in the spring (Fig 20c).

Fig. 4.30 Spatial correlation between the SOI and SLP for DJF during second focus period (1900-1950) for a 20CRv2c, b 20CRv3, c ERA-20C, and d CERA-20C 75

Fig. 4.31 As in Fig. 31, but for MAM

Fig. 4.32 As in Fig. 31, but for JJA 76

Fig. 4.33 As in Fig. 31, but for SON

Fig. 4.34 Spatial correlation between the SOI and SLP for DJF during first focus period (1950-2000) for a 20CRv2c, b 20CRv3, c ERA-20C, and d CERA-20C 77

Fig. 4.35 As in Fig. 35, but for MAM

Fig. 4.36 As in Fig. 35, but for JJA 78

Fig. 4.37 As in Fig. 35, but for SON

As discussed previously, ENSO influences high latitudes by interacting with the

PSA and the SAM, meaning that looking at linear correlations between SOI and SLP may not fully indicate the interactions that are present. To expand the understanding of influences from ENSO, spatial correlation maps for the focus periods is used. These spatial correlations indicate how teleconnections change between each period and the skill of the reanalyses to resolve them. For example, plots that show the correlation during the late twentieth century have a larger magnitude in the epicenter of ENSO than the early twentieth century. In the late twentieth century the largest correlation values in the Pacific Ocean are 0.8 to 1.0. In the early twentieth century these values drop drastically, while some reanalyses have the correlation value 0.6 to 0.8, the correlation 79 more commonly is 0.4 to 0.6. Interestingly, ERA-20C has the lowest correlations during this period, and there is not even an obvious ENSO signature.

These correlation values can also be utilized to look at the relationship between

ENSO and the ASL. In the late twentieth center, Figs. 4.34 to 4.37, there is a negative correlation between ENSO and the ASL region, which means during a positive ENSO event, the ASL will weaken. This pattern, while not seen in ERA-20C, is seen in the early twentieth century despite the reanalyses resolving weaker correlations between ENSO and SLP.

There are two likely explanations as to why reanalyses resolve lower correlations between ENSO and SLP in the early twentieth century. First, the reanalyses are resolving the pattern correctly and ENSO weakens in the early twentieth century, but this seems unlikely given the documented challenges of the reanalyses in the early 20th century.

Second, and more likely, the reanalyses fail to accurately resolve the early twentieth century even in the tropics. Based on these correlations ENSO does not have a strong influence everywhere at all times.

4.3.3 Influence from PDO

The PDO has been shown to cause large changes in connections between other climate modes and causes large climate variations globally (Gershunov and Barnett

1998). The PDO has shifted phases four times during the twentieth century, 1925, 1947,

1977, and the mid 1990s (Zhang et al. 1997; Mantua et al. 1997; Minobe 1997, Kayano and Andreoli 2007). This study has already shown a few cases where changes in patterns or correlations change around 1920, 1940, and 1970, which all correlate with shifts in the phase of the PDO. To get a better idea of how the PDO influences specific regions, a 80 running correlation between the midlatitude stations and the PDO is index is investigated

(Fig. 4.38).

Many regions have correlations to PDO that fluctuate. The region with the most constant fluctuation in correlations with the PDO is New Zealand. During DJF and MAM there are instances where every observation station changes correlation sign (Figs. 4.38a, b). In these seasons for example, a little before 1940, the correlation switches from positive to negative. Around this same period, New Zealand during SON (Fig. 4.38d) switches from a positive correlation to a negative. This occurs in JJA, but much weaker

(Fig. 4.38c). Australia, unlike New Zealand, is almost always negatively correlated to

PDO during DJF and MAM (Fig. 4.38a, b), while South Africa is nearly always positively correlated in DJF and JJA (Fig. 4.38a, c). The changes in the correlations could explain some of the shifts in SLP trends in Fig 4.1. Both South Africa and Australia experience small shifts in the SLP trends near 1920 and 1950,

81

Figure 4.38 Running correlation between observational data from midlatitude stations and the PDO index for a DJF, b MAM, c JJA, and d SON

Figure 40 shows the 30-year running correlation between Antarctic stations and the PDO. An interesting result of this calculation is the pattern evident in DJF (Fig.

4.39a). Before 1920 the correlation between the Antarctic region and the PDO is positive.

Afterwards, all the way until a little after 1960, the correlation is negative and then after

1960 the correlation returns to negative. The season MAM, while not as clear as DJF, also has important shifts in the PDO correlation (Fig. 4.39). Post 1920 the correlation between Antarctica and the PDO is positive at most stations. Between the 1920 and 1940 82 almost all stations are negatively correlated. What drives this change in the relationship between the PDO and Antarctic stations during DJF is unknown and warrants further investigation.

Near 1920 and 1940 many Antarctic stations experienced an increase in SLP during MAM, JJA, and SON, which correlates to shifts in the PDO (Fig. 4.15b, c, d). In

Fig. 4.39, the PDO and most stations are negatively correlated. During this period the

PDO was in its warm phase, or positive. A negatively correlated positive PDO would cause SLP values to rise across Antarctica.

Figure 4.39 Running correlation between reconstruction data from Antarctic stations and the PDO index for a DJF, b MAM, c JJA, and d SON 83

Investigating reanalyses skill in resolving the PDO and its influences is also important. Figures 41 through 48 show the spatial correlation between the reanalyses and the PDO during both focus periods. These correlations show the PDO epicenter in the

Pacific Ocean, mostly near the and the Northern Hemisphere. Despite the

Northern Hemisphere epicenter, it is investigated in this study because the PDO has been shown to have global influences and may be linked to patterns seen in this study

(Gershunov and Barnett 1998; Clem et al. 2015). Similar to the SOI and SAM plots, the correlation magnitudes are weaker in the early twentieth century likely due to lack of skill in the early twentieth century.

These correlations show weak correlations between the PDO and SLP in the high latitudes of the Southern Hemisphere. The largest correlation values in the late twentieth century occur during JJA (Fig 4.42). In the early twentieth century, the magnitude of correlation in the epicenter is weaker, but the correlations across the high latitudes does now change in magnitude. Based on similar plots the correlations decrease in magnitude in the early twentieth century because of reanalyses weak skill. Since the correlation was weak in both focus periods, it is hard to conclude if it is caused by poor skill during this time or the fact reanalyses show very little influence from the PDO in the high latitudes, though it is likely lower skill in the reanalyses. Similar to the late twentieth century plots, the early twentieth century plot also resolves the largest magnitude in correlations during

JJA (Fig. 4.46). It is also important to note that both the early and late twentieth century plots have some reanalyses resolving a positive trend during DJF (Fig. 4.40, 4.44). In the relationship between SLP and the PDO across the twentieth century there is some agreement. 84

Figure 4.40 Spatial correlation between the PDO index and SLP for DJF during first focus period (1950-2000) for a 20CRv2c, b 20CRv3, c CERA-20C, and d ERA-20C

Figure 4.41 As in Fig 41, but for MAM 85

Figure 4.42 As in Fig 41, but for JJA

Figure 4.43 As in Fig 41, but for SON 86

Figure 4.44 Spatial correlation between the PDO index and SLP for DJF during second focus period (1900-1950) for a 20CRv2c, b 20CRv3, c CERA-20C, and d ERA-20C

Figure 4.45 As in Fig 45, but for MAM 87

Figure 4.46 As in Fig 45, but for JJA

Figure 4.47 As in Fig 41, but for SON 88

Chapter 5: SUMMARY

Based on methods and data types utilized in this study, the late twentieth century is characterized by a negative SLP trend across Antarctic and a positive pressure

SLP trend in across the midlatitudes in all season except SON where there is possible influence from ENSO. This relationship is seen in the observations and reconstruction but not in the reanalyses, but since this is a result of atmosphere conserving mass and reanalyses do not conserve mass during this period, this is not surprising. It is also likely that reanalyses have more skill in the late twentieth century because of the larger amount of data available to constrain the output. While this is true, the reanalyses still resolve some naturally occurring variability, even in the early 20th century. Due to data issues, the early twentieth century is more difficult to confidently characterize. This study has shown that the majority of the variability is likely dominated by the complicated interactions of climate modes.

An important characteristic of the Southern Hemisphere during the entire twentieth century, which is not fully covered in this summary, is the influence of the

PDO. Many of the analyses done in this study indicated shifts in trends or relationships that match established shifts in the PDO. An important next step in better understanding variability across the Southern Hemisphere is to understand how the PDO influences other climate modes and variability. Understanding how PDO drives changes in teleconnections across the Southern Hemisphere will allow for a better analysis of the early twentieth century. 89

An important next step in better understanding variability across the Southern

Hemisphere is to understand specific weaknesses resolved in the reanalyses and to understand how the PDO influences these interactions. Understanding how PDO drives changes in teleconnections across the Southern Hemisphere will allow for a better analysis of the early twentieth century.

90

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