Antarctic Ice Sheet Dynamics and Contribution to Sea Level Rise During the Last Interglacial

Antarctic Ice Sheet dynamics and contribution to sea level rise during the Last Interglacial

Helen Millman

A thesis submitted in fulﬁlment for the degree of Doctor of Philosophy

Climate Change Research Centre School of Biological, Earth and Environmental Sciences University of New South Wales

Supervisors: Prof. Chris Fogwill, Prof. Chris Turney, Dr. Steven Phipps, Prof. Nick Golledge

August 2018

Thesis/Dissertation Sheet

Surname/Family Name : Millman Given Name/s : Helen Margaret Louise Abbreviation for degree as give in the University calendar : PhD Faculty : Science School : Biological, Earth and Environmental Sciences Antarctic Ice Sheet dynamics and contribution to sea level rise during the Thesis Title : Last Interglacial

Abstract 350 words maximum: (PLEASE TYPE) The magnitude of future sea level rise remains uncertain due to a lack of understanding of feedbacks and tipping points in the climate system and cryosphere. As proxy records and current observations have limited spatial and temporal coverage, numerical modelling is needed to explore these crucial areas.

It is thought that projected global mean surface temperatures will be ∼1-4°C above pre-industrial values by the end of this century, and whilst no past period truly reflects the potential future under anthropogenic climate change, past warm periods can be useful process analogues for future change. The Last Interglacial (LIG) was the last warm period before the present day, occurring 129-116 ka. It is thought that global mean sea level (GMSL) during the LIG was 5 to 10 m higher than present. With 60 m sea level equivalent (SLE) ice volume, the Antarctic Ice Sheets are the largest potential contributor to sea level rise, but they are also associated with the largest unknowns.

Climate models consistently underestimate the level of warming during the LIG, and ice sheet modelling studies have been unable to reconstruct the apparent LIG sea level shown in the palaeo-records without significant changes to the model physics. LIG CO2 levels from ice core records show a variation of up to σ4, where σ represents standard deviation. It can take many years for bubbles of trace gases to become enclosed in the ice and so these uncertainties may be even greater if peak CO2 is not captured due to low precipitation and long closure periods. This study uses both climate and ice sheet modelling to assess the impact of elevated greenhouse gas (GHG) concentrations on Antarctic climate and ice sheet dynamics under LIG orbital forcing.

Ice losses range from 16 to 648 cm SLE (sea level equivalent) over 20 kyrs of constant climate forcing, with the majority of this sea level rise stemming from the collapse of the West Antarctic Ice Sheet (WAIS). This mass loss was found to be driven by ocean-warming and is constrained by topography, with marine ice-sheet instability having significant impacts across key sectors of Antarctica.

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Abstract

Sea levels have been rising since the early 20th century due to the increase in global temperatures predominantly caused by anthropogenic forcing. Although sea level rise is one of the major challenges we face today, the magnitude of future sea level rise remains uncertain due to a lack of understanding of dynamic feedbacks and tipping points in both the climate system and cryosphere. As proxy records and current observations are fragmentary and have limited spatial and temporal coverage, numerical modelling can help to explore these crucial areas.

It is thought that projected global mean surface temperatures will be ∼1-4◦C above pre- industrial values by the end of this century, and whilst no past period truly reflects the potential future under anthropogenic climate change, past warm periods can be useful process analogues for future change. The Last Interglacial (LIG) was the last warm period before the present day, occurring 129-116k years ago. It is especially useful as a process analogue due to the relative abundance of proxy data from this time. Importantly, with amplified temperatures at high latitudes (polar amplification) global average temperatures during this period were up to 3◦C warmer than pre-industrial times. It is thought that global mean sea level (GMSL) during the LIG was 5 to 10 m higher than present. The Greenland Ice Sheet is believed to have contributed 0.6 to 4.3 m to LIG GMSL, and -0.2 to 0.4 m may be attributed to thermal expansion. With 60 m sea level equivalent (SLE) ice volume, the Antarctic Ice Sheets are the largest potential contributor to sea level rise, but they are also associated with the largest unknowns.

Climate models consistently underestimate the level of warming during the LIG, and ice sheet modelling studies have been unable to reconstruct the apparent LIG sea level shown in the palaeo-records without signiﬁcant changes to the model physics. LIG CO2 levels from ice core records show a variation of up to σ4, where σ represents standard deviation. It can take many years for bubbles of trace gases to become enclosed in the ice and so these uncertainties may be even greater if peak CO2 is not captured due to low precipitation and long closure periods. This study uses both climate and ice sheet modelling to assess the impact of elevated greenhouse gas (GHG) concentrations on Antarctic climate and ice sheet dynamics under LIG orbital forcing.

i The climate modelling aspect of this study uses CSIRO Mk3L to investigate the interaction between LIG orbital forcing, elevated greenhouse gas levels, sea ice cover, and the dynamics of the Southern Ocean. These climate model outputs are used to drive the PISM hybrid ice sheet model to discover the relationship between increased greenhouse gas levels under LIG orbital forcing and Antarctic ice sheet dynamics and sea level contribution, with a focus on tipping points and the impact of increased GHG concentrations.

Ice losses range from 16 to 648 cm SLE (sea level equivalent) over 20 k years of constant climate forcing. This study shows that a 5.3% increase in peak CO2 under LIG conditions is sufﬁcient to generate a 4.5 to 6.5 m contribution to sea level on a millennial scale, with the majority of this sea level rise stemming from the collapse of the West Antarctic Ice Sheet (WAIS). This mass loss was found to be driven by ocean-warming and is constrained by topography, with marine ice-sheet instability having signiﬁcant impacts across key sectors of Antarctica. A reduction of sea ice on the east coast and increased air temperatures lead to an increase in precipitation; some sectors, such as the Dry Valleys, see small mass gains of 1 to 5 cm SLE.

ii Acknowledgements

I’ll start by acknowledging the invaluable support (ﬁnancial, logistical, technical, personal and academic) of all four of my supervisors: Chris Fogwill, Chris Turney, Nick Golledge and Steven Phipps. Thanks also to Antarctic Logistics & Expeditions for paying me some money and allowing me to achieve my childhood dream of going to the toilet in Antarctica.

Thanks to everyone at the CCRC at UNSW, particularly my supervisory panel for their help and advice: Gab, Lisa, Jason, Katrin and Andrea; and to Bronwen for all the afternoon teas, ofﬁce help and storage space. The icy people on the 3rd ﬂoor at IMAS deserve a special mention for putting up with me for many months down in Hobart. Cheers to everyone at the ARC at VUW for lending me a desk for 3 months. A heartfelt thanks goes to all the people at Keele for accepting me as one of their own, especially everyone in the PhD lab, Alex Nobajas for the ArcMap refresher, and Rich Burgess for his technical support and teaspoon supply updates.

I’m also really grateful to Neil Glasser for telling me about this opportunity, and to Simon Cook for the encouragement he gave me in the dark post-MSc days.

Obvious gratitude goes to my parents; my Father for frequently telling me that I ought to get both a life and a proper job, and my Mother for telling him to be quiet. Finally, I’d like to thank Mr Rafﬂes for his top-level wooﬁng.

iii Contents

1 Introduction 1 1.1 Aims & Objectives ...... 4 1.2 Thesis structure ...... 4

2 Literature Review 5 2.1 Sea level change...... 5 2.1.1 Glacial isostatic adjustment...... 6 2.1.2 Dynamic topography ...... 7 2.2 Ice sheets ...... 7 2.2.1 Ice streams ...... 7 2.2.2 Ice shelves ...... 9 2.2.3 Controls on ice sheet dynamics ...... 11 2.3 The Last Interglacial ...... 15 2.3.1 Climate ...... 16 2.3.2 Sources of Last Interglacial sea level rise ...... 21 2.4 Ice sheet models: An overview ...... 22 2.4.1 Resolution and time-step ...... 23 2.4.2 Ice Flow Mechanics ...... 23 2.4.3 Basal sliding & bed strength ...... 28 2.4.4 Isostatic adjustment ...... 28 2.4.5 Grounding Lines ...... 29 2.4.6 Climate and surface mass balance ...... 30 2.5 Modelling past and future ice sheet contribution to sea level rise ...... 30

3 Methods 35 3.1 Model description ...... 35 3.1.1 Parallel Ice Sheet Model (PISM) ...... 35 3.2 CSIRO Mk3L ...... 36 3.3 Ice sheet model inputs ...... 37 iv 3.3.1 Tuning experiment inputs ...... 38 3.3.2 Climate anomalies ...... 42 3.3.3 Other boundary conditions ...... 44 3.4 Ice sheet model parameters ...... 47 3.4.1 Enhancement factors ...... 47 3.4.2 Yield stress ...... 47 3.4.3 Grounding-line ...... 48 3.4.4 Calving ...... 49 3.4.5 Bed deformation ...... 49 3.5 Experiment Setup ...... 49 3.5.1 Initial smoothing stage ...... 49 3.5.2 Intermediate stage ...... 50 3.5.3 Final evolution stage ...... 50 3.6 Model tuning ...... 50 3.6.1 Chosen parameter settings ...... 51

4 Modelled Antarctic Last Interglacial climate 53 4.1 Overview ...... 53 4.2 General Circulation Model (GCM) outputs ...... 54 4.2.1 Atmosphere ...... 55 4.2.2 Ocean ...... 58 4.2.3 Sea ice ...... 60 4.2.4 Ocean salinity ...... 61 4.2.5 Antarctic Bottom Water formation ...... 62 4.3 Discussion ...... 65 4.3.1 Sea ice cover, air temperatures & precipitation ...... 65 4.3.2 Ocean ...... 66 4.4 Conclusion ...... 67

5 Antarctic contribution to sea level rise during the Last Interglacial 69 5.1 Overview ...... 69 5.2 Climate perturbations ...... 70 5.2.1 Surface air temperature ...... 70 5.2.2 Precipitation ...... 70 5.2.3 Sea surface temperatures & salinity ...... 70 5.3 Contribution to sea level ...... 74 5.4 Areas of mass loss ...... 75 5.4.1 Lower estimates ...... 75 5.4.2 Higher estimates ...... 83 5.5 Ice dynamics ...... 90

v 5.6 Drivers ...... 92 5.7 High resolution experiments ...... 94 5.8 Discussion ...... 94 5.8.1 Low- vs. high-estimate experiments ...... 96 5.8.2 Comparisons with previous studies ...... 97 5.9 Conclusions ...... 98

6 Climate mitigation and the stability of the West Antarctic Ice Sheet 99 6.1 Overview ...... 99 6.2 Interglacial perturbation ...... 100 6.2.1 Ice volume response ...... 100 6.3 "Recovery" simulations ...... 100 6.3.1 Ice volume response ...... 101 6.3.2 Ice thickness change ...... 102 6.4 Discussion ...... 105 6.4.1 WAIS inroads ...... 105 6.4.2 Isostatic rebound ...... 106 6.4.3 Conclusions ...... 107

7 Conclusions 109 7.1 Palaeo-climate reconstruction ...... 109 7.2 The Cryosphere ...... 110 7.3 The future ...... 111 7.4 Areas of further study ...... 112 7.5 Summary ...... 113

A Appendix 115

B Appendix 117

C Appendix 119

List of Figures 179

Bibliography 183

vi Chapter 1

Introduction

Projected mean annual global surface air temperature changes for the end of this century range from 1.6◦C (representative concentration pathway (RCP) 2.6) to 4.3◦C (RCP8.5) above pre- industrial conditions, with ampliﬁcation of high-latitude temperatures leading to warming of 3◦C (RCP2.6) to 12◦C (RCP8.5) in the polar regions (IPCC, 2013). Sea level rise projections range from 0.26 to 0.55 m for RCP2.6, to 0.52 to 0.98 m under RCP8.5 by 2100 (IPCC, 2013). Golledge et al. (2015) modelled the long-term response of the Antarctic ice sheets to the four RCPs of the IPCC (2013) and found that multi-millennial loss can only be mitigated by limiting GHG emissions to RCP2.6. Higher-emissions scenarios lead to ice loss from Antarctica that would raise sea level by 0.6–3 m by 2300, and 5.2–9.31 m by 5000 CE. Over 10,000 years, a total global mean sea level (GMSL) rise of 25 to 52 m is possible, with much of this derived from the Antarctic ice sheet (Clark et al., 2016). A GMSL rise of 25 m would affect 19% of the 2010 global population (Figure 1.1) (Clark et al., 2016). Even without an increase in population in these areas over future millennia, mass migration on an unprecedented scale will likely accompany sea levels rise. It follows that increased knowledge of the Antarctic response to a warmer world is vital, however our ability to predict ice sheet responses to future climatic change is restricted by limited knowledge of past variations and their underlying forcing mechanisms (Pollard and DeConto, 2009; Hindmarsh, 1993). Greater understanding of Last Interglacial (LIG) climate and ice sheet dynamics is needed in order to reﬁne estimates of future, as well as past sea level rise (SLR).

The LIG, which is also referred to as Marine Isotope Stage 5e, occurred ∼129-116 ka (where present is deﬁned as 1950) (Harrison et al., 1995; Otto-Bliesner et al., 2006; Turney and Jones, 2010). Global annual average temperatures during this period were up to 3◦C warmer than pre-industrial times (deﬁned here as average conditions from 1850–1900) (Capron et al., 2014; Turney and Jones, 2010), with temperatures in East Antarctica peaking at ∼5◦C above the present approximately 128 ka (Jouzel et al., 2007). Although the Last Interglacial can not be considered a direct analogue for future change due to differences in orbital forcing and GHG concentrations,

1 Figure 1.1: The 2010 global population that would be directly impacted by a 25 m GMSL rise. This map shows the percentage of the population-weighted area for each country or mega-city (urban agglomeration with population 10 million or greater) below the projected long-term local mean sea level. From Clark et al. (2016). it can be thought of as a realistic case study that illustrates the responses of the Earth’s climate system which may be triggered by future warming (Fischer et al., 2018).

Global mean sea level (GMSL) and the rate of sea level rise (SLR) during the LIG are currently poorly constrained. Sea level relative to land surfaces can vary locally due to tectonic processes, gravity and other factors which must all be accounted for to produce an accurate reconstruction of past GMSL from proxy records. Although a standardised approach has been devised to enable better LIG GMSL estimates by using more precise measurements and including uncertainties (Rovere et al., 2016), this approach has not yet been used on a global scale.

It is thought that GMSL was at least 5m higher than present (Kopp et al., 2009; Dutton and Lambeck, 2012; Dutton et al., 2015). The most probable estimate is 6 m higher than present (Church et al., 2013), however, recent studies have suggested sea level would have been 2 m higher than this (Rohling et al., 2017). McKay et al. (2011) attribute -0.2 to 0.4 m to thermal expansion of the ocean, and model simulations and elevation changes from the NEEM (North Greenland Eemian) ice core suggest that the Greenland ice sheet (GrIS) contributed between 1.4m and 4.3m to GMSL rise during the LIG (NEEM Community Members, 2013). This indicates an input from the Antarctic ice sheets of 3.6 to 7.4 m, however, this is not yet conﬁrmed by palaeo-record or model evidence.

Climate models consistently underestimate the level of warming during the LIG (Lunt et al., 2013; Fischer et al., 2018), and ice sheet modelling studies have been unable to reconstruct the apparent LIG sea level shown in the palaeo-records (Fyke et al., 2011; Pollard et al., 2015). Uncertainties in the trace gas records prescribed as boundary conditions in models have been identiﬁed as one of the possible causes of error in the climate model simulations (Fischer et al.,

2 2018). It is possible that peak CO2 may not be captured in the ice core record due to low precipitation and long closure periods. LIG CO2 levels in the ice core records show a variation of up to σ4, where σ represents standard deviation (Köhler et al., 2017). This margin of error could have a huge impact on the simulation of LIG climate and ice sheet dynamics.

Ice sheets are an important component of the climate system, however, they remain one of the least understood. Drivers of ice sheet response include: climate-ice feedbacks, internal ice dynamics, solid earth deformation and basal conditions. The complex interaction of these processes with each other and their environment means that ice sheets can respond in a nonlinear fashion to linear forcings (Huybers, 2009) and their behaviour can be at odds with climatic perturbations (MacAyeal, 1993). For example, accumulation may increase due to warmer temperatures, offsetting the effects of ablation, and causing the ice sheet to grow. Ice sheet modelling plays a key role in the investigation of ice sheet sensitivity to a changing climate. This technique has a variety of approaches, from simple ﬂow-line models to fully-coupled higher- order models, which solve the full Stokes equation. Since modelling ice sheet dynamics is such a complex task, no single model can be the best at simulating every characteristic (Bindschadler et al., 2013), and ideally, output from a range of models with differing complexity should be considered.

Previous LIG ice sheet modelling studies (Fyke et al., 2011; Pollard and DeConto, 2009) have been unable to achieve the levels of melt from Antarctica indicated by the sea level record (Kopp et al., 2009; Dutton and Lambeck, 2012; Church et al., 2013; Rohling et al., 2017). Pollard et al. (2015) introduced hydrofracture and ice-cliff failure mechanisms to their model to induce the scale of ice sheet collapse found in the palaeo-records, however the inclusion of these processes is controversial (Fogwill et al., 2016).

Pollard and DeConto (2009) recommend that global climate models in combination with regional circum-Antarctic and sub-ice-shelf ocean modelling should be used to better ascertain the effects of Northern Hemispheric glacial cycles, orbital forcing and greenhouse gas concentrations on regional Antarctic conditions. This investigation aims to contribute to these efforts by examining the effects of increased greenhouse gas concentrations under LIG orbital forcing on the Antarctic ice sheets.

3 1.1 Aims & Objectives

This investigation aims to shed light on some of the unknowns of LIG Antarctica by examining the effects of increased greenhouse gas concentrations under LIG orbital forcing on the Antarctic climate and the Antarctic ice sheets.

The key objectives are as follows:

1. Explore Southern Ocean and Antarctic climate response to increased GHG concentrations under LIG orbital forcing;

2. Evaluate the sensitivity of the Antarctic ice sheets to these elevated CO2 concentrations over millennial time-scales using a hybrid ice sheet model;

3. Examine the tipping points of the West Antarctic Ice Sheet and evaluate it’s ability to recover from partial collapse.

1.2 Thesis structure

The ﬁrst part of Chapter 2 gives an overview of LIG climate and sea level, and the second part of Chapter 2 focuses on ice sheet models and their applications. Chapter 3 is a methods chapter which sets out why each model was chosen and explains the experiment setup. Chapter 4 describes the impact of increased greenhouse gas concentrations on a simulated LIG climate. This is built-upon in Chapter 5, where the outputs from the climate model are used to drive an ice sheet model to assess how sensitive the Antarctic ice sheets are to increased greenhouse gas concentrations under LIG climate conditions. The impact of climate mitigation and ice sheet recovery on Antarctic ice sheet dynamics is presented in Chapter 6. The links between all the ﬁndings of this study are discussed and conclusions are made in Chapter 7.

4 Chapter 2

Literature Review

The possibility that the West Antarctic Ice Sheet (WAIS) may have become unstable and could lead to >5 m rise in global mean sea level (GMSL) was ﬁrst raised by Hughes (1973, 1975).

This was later linked to elevated levels of atmospheric CO2 and polar ampliﬁcation by Mercer (1978). The importance of these studies was magniﬁed by the discovery of grounding line instability by Weertman (1974). These theories have been substantiated by current observations, which show rapid grounding line retreat in the Amundsen Sea sector of the WAIS (Rignot et al., 2014; Imbie Team, 2018).

In order to understand possible future ice sheet changes and sea level rise, it is necessary to examine how ice sheets have responded to previous interglacials. These warm periods are used as natural experiments that can shed light on how ice sheets may respond to future warming.

In this chapter, the Last Interglacial climate and sea level will be discussed, including proxy record and modelling reconstructions. The second part of this chapter introduces ice sheet modelling and explores how ice sheet models have been used to reconstruct past ice sheet dynamics and predict future sea level rise.

2.1 Sea level change

When considering coastal changes and palaeorecords, sea level is measured in relation to the surface of the solid earth (relative sea level). Shorter period variability is removed by giving the temporal average sea level for a particular location, or mean sea level. Mean sea level is usually spatially averaged to give global mean sea level (GMSL), however, sea level change is not uniform across the globe, and local sea level changes can be signiﬁcantly different to GMSL (Church et al., 2013).

Sea level can vary over a large range of spatial and temporal scales. Local sea levels can be affected by both ocean salinity and temperature changes, but only temperature changes can lead

5 to global mean sea level change (Church et al., 2010). This can be through thermal expansion, or changes in land ice volume.

Figure 2.1 shows how sea levels, global mean temperatures and atmospheric CO2 may have varied during past warm periods, as well as the possible fraction of mass loss from the Greenland and Antarctic ice sheets. See section 2.3.2 for discussion of LIG sea level estimates.

Figure 2.1: Peak global mean temperature, atmospheric carbon dioxide, maximum GMSL, and source(s) of meltwater. Light blue shading shows the uncertainty of GMSL maximum, and red shaded sections represent the possible fraction of mass loss from each ice sheet. From Dutton et al. (2015).

Sea levels can also be affected by glacial isostatic adjustment (GIA), and dynamic topography.

2.1.1 Glacial isostatic adjustment

Changes to ocean floor height and the Earth’s gravity field occur as a result of loading and unloading of the Earth’s crust as ice sheets grow and shrink during glacial-interglacial cycles (Farrell and Clark, 1976). This leads to variations in regional sea level. The effects of GIA dominate the spatial variability of sea level changes over millennial timescales, which can account for sea level change of several mm year −1 (Dutton et al., 2015). Due to this spatial variability, past sea levels are usually reconstructed using data gathered from tectonically stable locations situated far from past glaciation, known as far-field sites. However, research has shown that far-field sites may be affected by changes to the Earth’s rotation axis due to GIA (Dendy et al., 2017).

6 2.1.2 Dynamic topography

Dynamic topography is caused by variations in the flow of the Earth’s mantle. It can cause metre to multi-metre variability over timescales of 1 to 100 million years, and it can have a significant effect on both regional and global sea level changes (Dutton et al., 2015). The effects of dynamic topography on the palaeo-sea level record have only recently been considered and they remain difficult to quantify (Austermann et al., 2017). Stephenson et al. (2019) identified variable elevations across an 80km sea level marker for the LIG of 6.5 m at a far-field site in Madagascar. This site is not affected by GIA, and so these variations are attributed to dynamic topography (Stephenson et al., 2019). The effects of dynamic topography at each highstand will vary and have yet to be quantified. Capron et al. (2019) highlight the improvement of estimates of the height and timescale of past interglacial sea level highstands around the world as a research priority. This would require a detailed study of sea level markers at far-field sites.

2.2 Ice sheets

An ice sheet is an area of ice that is unconstrained by topography and covers an area greater than 50000 km2 (Benn and Evans, 2010). The Greenland and Antarctic ice masses are the only present day ice sheets. The ice masses of the Antarctic are split into two main sections by the Transantarctic Mountains: the East Antarctic Ice Sheet (EAIS), and the West Antarctic Ice Sheet (WAIS), the latter includes the Antarctic Peninsula. Ice shelves and ice streams are important components of the Antarctic ice sheet.

2.2.1 Ice streams

Ice streams are wide, relatively shallow (<1000 m), fast-ﬂowing ice avenues that are fed by complex tributaries and draw ice from the ice sheet interior and carry it to the ice sheet edge at >800 m/year (Figure 2.2). They are important to ice sheet dynamics and mass balance because they carry over 90% of the ice and sediment that is released from the Antarctic ice sheets (Bamber et al., 2000) and can be considered the "arteries" of an ice sheet (Bennett, 2003).

Ice streams can be constrained by topography (topographic ice streams) or by areas of slow ice sheet flow (pure ice streams), or they may be a combination of both types. Ice streams have a low driving stress (∼10 kPa) and fast flow is possible because they have a slippery bed of soft, deformable sediments. (Truffer and Echelmeyer, 2003; Winsborrow et al., 2010). The mechanisms that control ice stream flow are both complex and numerous. Livingstone et al. (2012) compiled a list of all of these controls, and they include: oceanic temperature (Payne et al., 2004; Turner et al., 2017), sea-level changes, surface air temperatures (Parizek and Alley, 2004), ocean tides (Rosier and Gudmundsson, 2018), subglacial topography and topographical

7 pinning points (Schoof, 2007), grounding zone wedges, basal hydrology (Stearns et al., 2008; Anandakrishnan and Alley, 1997), thermodynamics, and the size of the drainage basin.

8 Ice stream margins can migrate and ﬂow can shut down and restart. When ice stream ﬂow stagnates, this can cause ice shelves to thin and even break-up, which can have a dramatic effect on the grounded ice (see Section 2.2.2) (Joughin and Tulaczyk, 2002). This potential for instability makes ice streams a key area of study.

Figure 2.2: Ice surface velocity map showing the ice streams of Antarctica. Adapted from Rignot et al. (2011).

2.2.2 Ice shelves

The Antarctic ice shelves are ﬂoating extensions of grounded ice that fringe 75% of the continent’s coastline, and cover an area roughly the size of Greenland (Figure 2.3) (Rignot et al., 2013). They have a strong effect on the dynamics of the ice sheet and also play a role in bottom water formation. Where ice shelves collapse, ice stream velocity can increase substantially due to the loss of back-stress (Benn et al., 2007b), and so the response of tributary glaciers to an ice-shelf disintegration event is a key factor when considering sea-level rise.

Iceberg calving accounts for the majority of mass loss from the Antarctic ice sheets; however, the mechanisms that control it are poorly understood. Benn et al. (2007b) identiﬁed four mechanisms for calving: stretching due to large-scale velocity gradients causing crevasses to penetrate deeper into the ice; unbalanced stress at unsupported ice cliffs, whereby ice rheology and the geometry of the ice margin are key controls; basal melting, particularly if melt is

9 Figure 2.3: The Antarctic ice shelves. The pie charts show the percentage of passive ice shelf area which can be lost without causing significant dynamic changes. From Fürst et al. (2016). concentrated in a given area; and the effect of buoyant forces. The main triggers were thought to be increases in longitudinal strain rates and decreasing ice thickness (Benn et al., 2007a). Alley et al. (2008) support this view, suggesting that along-flow ice-shelf spreading is the dominant control on iceberg calving and that buttressing is key to maintaining a stable ice shelf. Basal melt is not only important as a mechanism for calving but is a key process of mass-loss in its own right. This process has a particularly significant impact on the retreat of grounding lines and overall thinning of the ice-shelves (Walker et al., 2008; Pritchard et al., 2009).

Both mechanical and rheological structures within the ice shelves also have an effect on their retreat. After the collapse of Larsen B, Glasser and Scambos (2008) inferred that suture lines between ﬂow units created weaknesses in the structure of the ice-shelf, contributing to the speed of it’s break-up; this was further compounded by meltwater pools on the surface of the ice shelf. MacAyeal et al. (2003) proposed a mechanism of ice shelf fragment capsize, from observations of the disintegration of the Larsen A and B ice shelves, which could lead to sustained and accelerated breakup. The process requires ice shelf fragments to have a greater length than width, and the balance of force and the balance of torque also have an inﬂuence. This

10 mechanism is also thought to have had an important inﬂuence in the break-up of the Wilkins ice-shelf in 2008 (Humbert and Braun, 2008; Scambos et al., 2009).

Sea ice has an inﬂuence on ice-shelf stability, as it protects the shelves from ocean swell (Massom et al., 2018). Modelling has also shown that decreased winter sea ice formation stratiﬁes the water column and allows warm bottom water to intrude into ice shelf cavities (Naughten et al., 2018).

The sensitivity of ice shelves to warmer climates is largely dependent on geometry, with some shelves exhibiting greater vulnerability to warming than others. The removal of only a small portion of an ice shelf can have consequences for the dynamics of grounded ice, and ice shelf thinning can also have a far reaching impact. Enhanced outﬂow from the Bindschadler Ice Stream has been found in response to thinning on the opposing side of the Ross ice shelf, over 900 km away (Reese et al., 2017). Fürst et al. (2016) used the Elmer/Ice model to identify ice shelves which have large passive portions which can be lost without loss of buttressing potential, and ice shelves which have a small passive portion that are likely to cause signiﬁcant dynamic changes if their ice fronts retreat. The average area of passive ice shelf across Antarctica was found to be 13.4%. The most vulnerable sectors were the Bellinghausen Sea (5.3%) and the Amundsen Sea (7.9%), and the most robust ice shelves were in the East Indian sector (25.2%) and the West Indian sector (19.6%) (Figure 2.3).

2.2.3 Controls on ice sheet dynamics

Although climate is the key control on ice sheet dynamics, topography and isostasy are also important inﬂuences. This section will explore each of these ice sheet boundary conditions.

Climate

Ice sheets interact with all of the major components of the climate system and are principally controlled by changes in climate. They may be thought of as a ‘natural climate-meter’, as they respond, not only to temperature, but to many other climate variables, including precipitation and global ocean circulation (Vaughan et al., 2013).

Glaciers and ice sheets can only develop and survive where temperatures are cool enough for annual accumulation to exceed annual ablation. The difference between net accumulation and net ablation gives the surface mass balance (SMB). Atmospheric controls on SMB include: surface air temperature, surface relative humidity, windspeed, radiation ﬂuxes, and surface albedo (Cuffey and Paterson, 2010). Ice-ocean interaction affects mass balance through wave stresses (Massom et al., 2018), which has an impact on ice shelf calving rates over short temporal scales. Over much longer temporal scales, thermal ocean forcing drives basal melt of ice shelves and grounding line erosion (Pritchard et al., 2012; Rignot et al., 2002; Rignot, 2006).

11 Antarctica is characterised by extremely cold surface air temperatures and low relative humidity as the strong, prevailing westerlies that encircle Antarctica prevent warmer, wetter low-latitude atmosphere from reaching the continent. These winds develop due to the steep gradient between the warm, high-pressure areas over the subtropics and the cold, low-pressure areas over the poles. The westerlies winds tend to intensify and move towards the pole under warm climates.

This movement of the westerlies towards the poles can cause CO2 to be released from the oceans, leading to a positive warming feedback (Anderson et al., 2009).

The Westerlies in part drive the circulation of the Southern Ocean (Figure 2.4). Their increasing strength can lead to incursions of warm (up to 4◦ above freezing) Circumpolar Deep Water (CDW) across the continental shelf (Thoma et al., 2008; Jenkins et al., 2010). This process can dramatically increase the temperature of ocean water at ice sheet grounding lines, and is one of the major drivers of ice shelf melt (Pritchard et al., 2009; Spence et al., 2014; Turner et al., 2017).

Figure 2.4: A schematic showing Southern Ocean circulation and Antarctic ice-ocean interaction. From National Research Council (2011).

The Southern Ocean plays a pivotal role in global ocean circulation. The lack of continental barriers means that the Antarctic Circumpolar Current (ACC) is able to ﬂow unobstructed, forming a point of exchange between the three major oceans. The strength of Antarctic Bottom Water (AABW) formation and its northward ﬂow, are key processes which govern Global Thermohaline Circulation. Of the key sources of AABW, the Weddell Sea produces the coldest bottom water, which makes Atlantic Meridional Overturning (AMOC) particularly important to global ocean circulation.

12 Topography

One of the principal constraints on the dynamics of the Antarctic ice sheets is topography (Joughin and Alley, 2011). The major part of the West Antarctic Ice Sheet (WAIS) and parts of the East Antarctic Ice Sheet (EAIS), particularly between 105◦E and 165◦E, are grounded below sea-level and so are vulnerable to sea-level rise (Figure 2.5). The bed in the centre of the WAIS is more deeply depressed than the edges and so the bed slopes inland from the grounding line.

There are two hypothesised processes that may lead to the collapse of the WAIS: Marine ice-sheet instability (MISI), and the controversial marine ice-cliff instability (MICI). MISI is a self-sustaining retreat of the grounding line in areas where the bedrock slopes downward inland; a grounding line cannot be stable on a reverse-slope gradient. This process is triggered by ice-shelf thinning or collapse, and satellite and modelling evidence indicates that this process is currently underway in West Antarctica. MICI is a self-sustaining retreat in areas where the ice is >100 m above the ocean surface. There is no observational evidence that this process occurs, however, it has been included in some ice sheet models (DeConto and Pollard, 2016).

The MISI hypothesis is based on the observation that a grounding line on a reverse slope bed will be unstable (Pattyn, 2018). Huybers et al. (2017) performed ﬂow-line simulations for WAIS ice streams and found that many of the Weddell Sea sector ice streams, which have upward-sloping basal topography and strong lateral support, are more stable than those in the Amundsen and Ross Sea sectors. Current observations, which show rapid grounding line retreat in the Amundsen Sea sector of the WAIS, support the MISI hypothesis (Rignot et al., 2014; Imbie Team, 2018).

13 Figure 2.5: Antarctic basal topography. Areas below sea level are shaded in purple, and areas above sea level are orange. Adapted from Bedmap2 (Fretwell et al., 2013).

Areas of high-altitude are more stable, and it has been suggested that the Ellsworth Mountains represent one of the seeding areas where the WAIS originated, and may also act as a pinning point during periods of ice sheet retreat (Bentley et al., 1960; Bamber et al., 2009; Joughin and Alley, 2011; Ross et al., 2013). Bamber et al. (2007) highlight the Ellsworth Mountain range as an area of glacial stability connected to the EAIS plateau. This Ellsworth Mountain Ice Cap, as well as the Marie Byrd Land Ice Cap and the Antarctic Peninsula are identiﬁed points which may survive partial collapse of the WAIS. Although this hypothesis has not been proven (Bamber et al., 2009; Bradley et al., 2013), the Ellsworth Mountains may hold the oldest ice in West Antarctica. Since few ice cores in the WAIS have captured the LIG, this area could be an important source of proxy data. If the Ellsworth Mountain uplands can survive warm periods, WAIS contribution to GMSL rise is constrained to ∼3.3 m above present (Hein et al., 2016).

Until recently, the EAIS was considered immune to change, however recent studies based on improved topographical data have found that this is not the case (Mengel and Levermann, 2014; Fogwill et al., 2014; Bradley et al., 2013; Overpeck et al., 2006). Airbourne radio-echo sounding (RES) studies have found an extensive system of lakes beneath the EAIS (Siegert et al., 2005; Wingham et al., 2006), and associated dynamic behaviour of EAIS outlet glaciers has been observed (Stearns et al., 2008). The Aurora and Wilkes basins are grounded below sea level and so are vulnerable to marine ice sheet instability, and Mengel and Levermann (2014) show that grounding line instability at the margin of the Wilkes Basin can lead to runaway retreat and

14 GMSL rise of 3-4 m.

Isostasy

The loading and unloading of the Earth’s crust as the ice sheet grows and shrinks also has an impact on ice dynamics. The elevation of the ice surface can be decreased as the additional mass of an ice sheet causes the Earth’s lithosphere to sink into the asthenosphere. The isostatic depression can also increase the area of ice that is grounded below sea level.

Due to crustal rigidity, the ice sheet load is spread 150-180 km beyond its margin, causing a peripheral depression. Beyond this depression, the displaced asthenosphere forms a fore-bulge 250-280 km from the ice sheet margin (Benn and Evans, 2010). This bulge collapses when the ice sheet recedes and moves towards the former ice centre (Walcott, 1970). During glaciation and deglaciation, there is both an instantaneous elastic response to ice changes and a delayed, visco-elastic response that can take 10000 to 20000 years and may never reach equilibrium over a glacial-interglacial cycle (Walcott, 1970). Present day gravitational signal is still affected by the last glaciation.

Ice stream stagnation and reactivation cycles in the Siple Coast region are thought to contribute to glacial isostatic adjustment (GIA) and associated present day bedrock uplift (Nield et al., 2016). Although much of the WAIS is grounded below sea level, glacio-isostatic adjustment (GIA) and sea level fall near the grounding line can potentially have a stabilising inﬂuence on the ice sheet (Gomez et al., 2015). In the Amundsen Sea Embayment, for instance, Barletta et al. (2018) have observed rapid bedrock uplift in response to mass loss, that may trigger a stabilisation feedback.

2.3 The Last Interglacial

For at least the past 2.6 million years, the Earth has experienced a sequence of glacial and interglacial conditions primarily driven by cyclical changes in the planet’s orbit and axis which are collectively known as Milankovitch cycles. These cycles are governed by three variables. Firstly, the eccentricity of the Earth’s orbit varies from almost circular, to elliptical, and back again with a periodicity of 100,000 years. Secondly, the tilt of the earth’s axis relative to the orbital plane, also known as the obliquity of the ecliptic, ﬂuctuates on a 41,000 year cycle. Thirdly, the direction of the tilt of the Earth’s axis orbiting the sun relative to the distant stars, operating over a quasi-23,000 year cycle. The latter affects the timing and variability of the seasons, and is referred to as the precession of the equinoxes (Lowe and Walker, 2014).

The eccentricity of the Earth’s orbit largely controls the amount of solar radiation received, while obliquity and precession determine how it is distributed. The response of the various

15 components of the Earth’s climate system to these changes may be magniﬁed or reduced by feedback loops, with ice sheets playing a key role (Benn and Evans, 2010). Ice cores provide isotope records which capture the Pleistocene glacial-interglacial cycles (Figure 2.6). See Section 2.3.1 for further discussion of the Antarctic ice core records.

Figure 2.6: Time series of Dome C, Dome F and Vostok ice core δ records from preindustrial times to 336.5 kyr ago. From Sime et al. (2009).

The Last Interglacial (LIG) was the penultimate warm period before present day, occurring 129k-116k years ago. In contrast to previous interglacials, there is a relative abundance of global proxy data for the LIG, which makes it useful for model-data comparisons (Otto-Bliesner et al., 2013).

2.3.1 Climate

During the LIG, the Earth’s orbit was characterised by a relatively high obliquity and eccentricity, as well as a precessional element with perihelion occurring during the boreal summer (Laskar et al., 2004; Yin and Berger, 2010; Lunt et al., 2013). Natural variations in greenhouse gases also drove LIG climate change, with a greenhouse gas (GHG) peak between 129 and 128 ka (Siegenthaler et al., 2005; Loulergue et al., 2008; Lunt et al., 2013). There have been various attempts to reconstruct LIG climate using both proxy records and modelling, with global temperature estimates ranging from no signiﬁcant difference to 2◦C warmer than present day.

Turney and Jones (2010) completed the ﬁrst global quantiﬁed LIG annual temperatures from 263 ice, marine and terrestrial records from across the globe. This study concludes that global

16 average temperatures were approximately 1.9◦C warmer than pre-industrial levels (Turney and Jones, 2010). This estimate is affected by poor spatial coverage and biases within the proxies, particularly in Antarctica.

Through a combination of marine sediment core data and GCM simulations, McKay et al. (2011) estimated that peak global SSTs during the LIG were 0.7 ± 0.6◦C warmer than pre-industrial records. Further analysis of marine sediment cores by Hoffman et al. (2017) found that peak global SSTs correspond to present day temperatures at 0.5 ± 0.3◦C warmer than pre-industrial records. However, SST increases vary regionally and there is extremely poor data coverage for the Southern Ocean. Capron et al. (2014) estimate Southern Ocean SST were 1.7 ± 2.5◦C warmer than present due to polar ampliﬁcation.

Orbital insolation variations play a key role in Pleistocene climate change (Schulz and Zeebe, 2006). The precessional component is out of phase between the Northern and Southern hemispheres, which means that there is a seesaw effect that can cause strong seasonal differences between the hemispheres. This seesaw effect may be exacerbated by a variety of mechanisms, from large-scale processes, such as changes in greenhouse gas concentration (Manabe and Broccoli, 1985), to local/regional forcing factors, such as sea ice changes in the northern (Bauch et al., 2011) and southern hemispheres (Kim et al., 1998). However, the seesaw can also be dampened by Atlantic thermohaline circulation (Stocker, 1998; Stocker and Johnsen, 2003). Denton et al. (2010) hypothesise that the hemispheres can be coupled, despite out of phase components, due to the poleward shift of the Southern Hemisphere westerlies and the oceanic bipolar seesaw.

Although there is a broad consensus that Antarctica experienced earlier warming than other parts of the world during the LIG (Turney and Jones, 2010; Capron et al., 2014), the distribution of warming is uncertain. Analysis of ice and marine core records by Capron et al. (2014) found greater temperature changes over the ice sheet surface than the surrounding sea surface. This was attributed to land-sea contrast and albedo feedbacks. Temperatures were warmer for longer in the Southern Hemisphere than in the Northern Hemisphere, with the Southern Hemisphere warming and cooling before the Northern Hemisphere (Capron et al., 2014; Kim et al., 1998). Bakker et al. (2013) highlight the key climate feedbacks that inﬂuenced the evolution of LIG temperatures: sea-ice feedback, changes in the strength and conﬁguration of the AMOC, Northern Hemisphere continental ice sheets, and changes in the monsoon system.

Last Interglacial ice core records

The Antarctic ice core records have allowed reconstructions of Antarctic temperature over the past 800,000 years. Temperature dictates the concentration ratios of heavy to light oxygen and hydrogen isotopes in precipitation and so an ice core can provide a long temperature record. Bubbles of trace gases trapped in the ice core provide a record of past atmospheric compositions,

17 enabling the reconstruction of historic GHG concentrations.

Antarctic ice cores which capture the LIG include the Vostok (Jouzel et al., 1993; Petit et al., 1999), Dome C (Jouzel et al., 2007), Dronning Maud Land (EPICA Community Members, 2006), Dome Fuji (Watanabe et al., 2003), Taylor Dome (Grootes et al., 2001), and TALDICE (Talos Dome) (Stenni et al., 2010) from the EAIS; and the Mount Moulton horizontal ice core in Marie Byrd Land, and the Fletcher Promontory in the Weddell Sea sector of the WAIS (Figure 2.7).

Figure 2.7: Location of ice cores which contain a record of LIG climate.

The relationship between temperature and the isotopic composition of the ice has been found to be non-linear and also varies between ice core sites (Sime et al., 2009). A number of climatic and glaciological conditions can affect the ice core isotopic composition. Climatic impacts include changes in evaporation rates and air mass distillation history, as well as local conditions such as precipitation intermittency. These conditions are affected by orographic effect and circulation. Glaciological impacts include changes in ice surface elevation at the ice core site, and ice origin (Masson-Delmotte et al., 2011).

Correct interpretation of ice core records requires accurate knowledge of the precipitation rate. Current observations show that 60-80% of accumulation occurs over only a few days each year during "high-precipitation events" (Schlosser et al., 2008). These events are linked to ampliﬁcation of Rossby waves, causing increased meridional ﬂow patterns which bring in warmer, moist air from lower latitudes (Masson-Delmotte et al., 2011). High precipitation events tend to occur when surface temperatures are ∼10◦C warmer than the mean, which can introduce bias to the ice core record (Noone and Simmonds, 1998). For most of the year, Antarctic

18 precipitation is in the form of diamond dust, however this only accounts for 20-40% of the annual accumulation (Schlosser et al., 2008).

With such low accumulation, it can take hundreds of years for bubbles to be enclosed in the ice.

Differences of up to 6 ppm have been found in the CO2 records from the Law Dome, EPICA Dronning Maud Land and WAIS Divide Antarctic ice cores (Ahn et al., 2012). Although these offsets are currently unexplained, they could be due to processes associated with the ice core drill site, such as bubble enclosure, or differences in laboratory calibration (Köhler et al., 2017).

Ablation through sublimation and snowdrift must also be considered when interpreting the ice core record. There is large regional variation due to the impact that local wind speeds and relative humidity have on ablation rates (Frezzotti et al., 2004). This regional variation would have been even greater during past warm periods, further increasing bias (Masson-Delmotte et al., 2011).

During interglacials, the isotopic records in east Antarctic ice are less sensitive to temperature change (Sime et al., 2009). Sime et al. (2009) found that previous interglacial temperature estimates were too low, and could have been more than double previous estimates, at over 6K higher than present. However, Bradley et al. (2013) explored the effects of altitude on the δD signal in the EAIS ice cores and found that a signiﬁcant amount of the LIG warming signal in the ice core record could be due to elevation changes alone. Differing interpretations of the ice core record have also lead to a debate over the drivers of Antarctic warming.

To give one global greenhouse gas record, data from all of the world’s ice cores are merged into one through spline smoothing and age modelled tuning. This method gives relative uncertainties of 10%, in addition to uncertainties in the individual records (Köhler et al., 2017). There are currently no ice core records from Greenland which encompass the LIG in its entirety, and few records from the WAIS, therefore numerical modelling is necessary to build a more complete picture of global climate during this period. Although using one record would improve precision and details that are lost through spline interpolation would be retained, it would also reduce the accuracy of the model. Merging the records reduces the impact of an anomaly in any one record.

Palaeoclimate Modelling

Although there are ﬂaws in the physical record of LIG climate (Capron et al., 2017; Dutton et al., 2015), there is greater availability of data for this period than for any previous interglacials. This makes it the most suitable for climate model-data comparison studies.

The Palaeoclimate Model Intercomparison Project (PMIP) uses LIG climate snapshots to test the strengths and weaknesses of a variety of climate models (Lunt et al., 2013). For these experiments, greenhouse gas concentrations were taken from ice cores (Lüthi et al., 2008;

19 Loulergue et al., 2008; Spahni et al., 2005), and orbital constraints were taken from Laskar et al. (2004). Some of the models used orbital constraints from Berger and Loutre (1991), but the difference between insolation values for the datasets is less than 0.1%. The snapshots were taken at 130, 128, 125 and 115 ka and they represent equilibrium conditions in a 1000 year window.

The models included in the study vary in complexity, from the GCMs used in the most recent IPCC report (COSMOS (Jungclaus et al., 2006) and MIROC (K-1 Model Developers, 2004)), the GCMs used in the IPCC Fourth Assessment Report (CCSM3 (Collins et al., 2006) and HadCM3 (Gordon et al., 2000)), to Earth System Models of Intermediate Complexity (EMICS) such as LOVECLIM (Goosse et al., 2010), CLIMBER (Petoukhov et al., 2000) and CSIRO Mk3L (Phipps et al., 2011). Three versions of CCSM3 were used: CCSM3-LLN, CCSM3-Bremen and CCSM3-NCAR. CCSM3-Bremen included dynamic vegetation, and CCSM3-NCAR runs at a higher resolution. There are two versions of COSMOS: COSMOS-AWI and COSMOS-MPI. COSMOS-MPI uses dynamic vegetation, and COSMOS-AWI uses ﬁxed pre-industrial vegetation (Lunt et al., 2013). Some of the experiments did not use the greenhouse gas concentrations speciﬁed by PMIP, with some models running with constant greenhouse gas concentrations while others used independently developed changes, however, these experiments can still give useful insights into model response (Lunt et al., 2013).

All the climate models in the Lunt et al. (2013) study struggled to capture the magnitude of warming, especially in the high southern latitudes. Proxy records suggest warming of up to 5◦C in Antarctica, but the ensemble mean of all the model simulations shows warming of less than 1◦C. Even the CCSM3-NCAR model (Otto-Bliesner et al., 2013) cannot reproduce the LIG warming recorded in the ice cores, despite having one of the smallest errors for the simulation of pre-industrial surface air temperature (Lunt et al., 2013). Where climate model outputs are used to drive ice sheet models, this inability to recreate warming will lead to an underestimate of melt.

Pedersen et al. (2016) built upon the above by using the EC-Earth global climate model to simulate LIG climate at a much higher resolution than any of the models used by Lunt et al. (2013). The PMIP LIG protocol for the 125 ka snapshot was used to allow a comparison with the previous experiments. The simulated LIG temperature changes are reportedly closer to the proxy records reported by Capron et al. (2014) than those of previous studies. Importantly, Pedersen et al. (2016) found the loss of sea ice had an important effect on warming in the North Atlantic and Southern Ocean due to increase shortwave absorption.

Bakker et al. (2013) perform transient climate simulations rather than the snapshots used in other studies. Simulations for the LIG were run on seven different Earth System Models of Intermediate Complexity (EMICS). They found that model complexity and resolution is relatively unimportant over millennial timescales and large spatial scales. The importance of sea ice feedbacks, AMOC, Northern Hemisphere ice sheets and the monsoon system were

20 highlighted as key to the evolution of global LIG temperatures (Bakker et al., 2013).

Building on the above studies, Bakker et al. (2014) found a model-data mis-match for the LIG over mid-to-high latitudes in the Southern Hemisphere. This suggests that either the models are unable to resolve feedbacks associated with sea ice cover and Southern Ocean up-welling, or that interglacial Southern Hemisphere temperatures are driven by mechanisms which are not included in the simulations, such as ice sheets. The use of models that can simulate stable water isotopes to allow for a direct comparison between models and proxy records was recommended (Bakker et al., 2014). Although Gierz et al. (2017) have started to make progress by enhancing an Earth system model with a stable isotope diagnostic, they are yet to perform a detailed model-data comparison.

2.3.2 Sources of Last Interglacial sea level rise

Past GMSLs have been assessed using a range of approaches, including both empirical and modelling studies. Observational investigations into the physical sea level record may analyse a wide range of sea level indicators across a large number of sites across the world (Kopp et al., 2009), or they may investigate tectonically stable far-ﬁeld sites (sites located far from glaciation, both past and present), (Dutton and Lambeck, 2012; Blanchon et al., 2009).

Kopp et al. (2009, 2013) analysed a large database of LIG sea level indicators from all over the world. Accounting for GIA effects, uncertainties in the geochronology, and regional tectonic uplift and subsidence, including dynamic topography Kopp et al. (2013) found that GMSL was 6.4 m (95% probability) and 7.7 m (67% probability) higher than present, and with a 33% probability that it exceeded 8.8 m.

Dutton and Lambeck (2012) used tectonically stable far-ﬁeld sites to produce an estimate of 5.5 to 9 m LIG GMSL. These results are consistent with the ﬁndings of Kopp et al. (2009, 2013), and from them, Church et al. (2013) concluded that the most probable estimate is 6 m higher than present with a rate of rise between 2 m kyr–1 and 7 m kyr–1.

The sources of this sea level rise are a subject of contention. A modelling study by McKay et al. (2011) attributes -0.2 to 0.4 m to thermal expansion. Model simulations by Stone et al. (2013) indicated a 90% probability that Greenland contributed at least 0.6 m, and a less than 10% probability that Greenland contributed over 3.5 m to sea level during the LIG. Model simulations and elevation changes from the NEEM (North Greenland Eemian) ice core suggest that the Greenland ice sheet (GrIS) contributed between 1.4m and 4.3m to GMSL rise during the LIG (NEEM Community Members, 2013). Although the studies give different ranges, they all imply a substantial input from Antarctica of up to 8.8 m.

There is considerable debate over which sectors of Antarctica are the most vulnerable to

21 collapse. The possibility of future WAIS collapse has been widely discussed (Hughes, 1973, 1975; Mercer, 1978; Weertman, 1974). Early work proposed that WAIS collapse could lead to a 5-6 m rise in GMSL (Tol et al., 2006). However, Bamber et al. (2009) used bedrock elevation datasets to constrain possible WAIS contribution at 3.3 m. This is supported by geomorphological evidence and cosmogenic nuclide data from the Southern Heritage Range of the Ellsworth Mountains, which suggests that an Ellsworth Mountains Ice Cap survived previous deglaciations (Fogwill et al., 2012; Hein et al., 2016). Isotopic evidence from ice cores has been found to indicate a collapse of the WAIS during the LIG (Otto-Bliesner et al., 2013; Holden et al., 2010; Steig et al., 2015). Holloway et al. (2016) argue that the WAIS survived the early LIG, and changes in the ice core isotopic signal were caused by a reduction of winter sea ice, however, they do not rule out a WAIS collapse in the late LIG. If a signiﬁcant contribution to sea level did not come from the WAIS, it is likely that the EAIS also experienced mass loss.

Bradley et al. (2013) derived LIG elevation changes from ice core signals (Taylor Dome, TALDICE and EPICA Dome C) and found that partial collapse of the EAIS may have occurred during the LIG, leading to a signiﬁcant rise in sea level. The Wilkes and Aurora Basins are highlighted as sections of the EAIS that are vulnerable to collapse (Bradley et al., 2013). Pingree et al. (2011) hypothesised that the Amery section of the EAIS could have also collapsed during the LIG, matching the WAIS contribution to GMSL, however this has not been supported by observational or modelling evidence (Shakun et al., 2018).

Some studies have suggested that increased temperatures can cause the EAIS to gain mass (Zwally et al., 2015). However, analysis of the coral highstands of Western Australia indicates that there may have been some contribution to sea level rise from the EAIS, as well as the WAIS (O’Leary et al., 2013), implying that any mass gains were not sufﬁcient to offset the losses. Hay et al. (2014) used the distinct geographic patterns of sea-level change to identify the “ﬁngerprints” of the Greenland Ice Sheet, WAIS and EAIS contributions to sea level rise. They highlight the need for further investigation into the contribution of both the west and east Antarctic ice sheets to LIG sea level rise in order to identify the sources of a possible double peak in eustatic sea level.

The often conﬂicting interpretations of limited LIG proxy records mean that ice sheet modelling studies play a key role in investigating which sectors of the Antarctic ice sheets are most vulnerable to change.

2.4 Ice sheet models: An overview

The ﬁrst continental-scale ice sheet models were developed in the early 1990s. The models were coarse-resolution (40 km) and could only simulate ice deformation through basal shearing. Due to these limitations, the modelled ice sheets had a slow, diffusive response to climate changes

22 (Pattyn, 2018). These early models directly contradicted the theories of WAIS collapse put forward by Hughes (1973), Mercer (1978) and Weertman (1974). Schoof (2007) was the ﬁrst to mathematically prove Weertman’s MISI theory. In the decade since, ice sheet model development has advanced further and simulated ice dynamics more closely resemble observed ice sheet behaviour.

There are a wide variety of approaches to ice sheet modelling, encompassing simple ﬂowline models to fully-coupled higher-order models, which solve the full Stokes equation (see Section 2.4.2). Since modelling ice sheets is such a complex task, no single model can resolve all aspects. As a result, different modelling approaches must be taken, based on the research question to be tackled. Here, the characteristics of different models and their advantages and disadvantages for application to a continental ice sheet will be outlined. A list of the models discussed and some of their features are summarised in Table 2.1.

2.4.1 Resolution and time-step

To model ice dynamics, algorithms are run on a regular grid of nodes using either the eulerian flow field, finite element or finite difference techniques for discretisation of the ice flow equations. The grid consists of two horizontal dimensions and a vertical dimension of often unequal layers. Horizontal grid resolutions vary from <5km to 50km, with between 10 and 100 vertical layers, which can be concentrated at the base of the ice sheet where most physical processes take place (Huybrechts, 2009).

Higher-order models tend to have an irregular horizontal grid. For example, Elmer/Ice has an adaptive horizontal grid, with a resolution of 1km at the margins of the ice sheet and 70km in the interior (Bindschadler et al., 2013). A variation of this is ISSM which has an anisotropic grid, where the fast ice streams (3km) are more highly resolved than the slow-ﬂowing interior (15km) (see Table 2.1) (Bindschadler et al., 2013).

Importantly, the ﬁner the grid resolution, the shorter the time-step. PISM may be run at a 5km resolution, with an adaptive time-step of 15 days (Bueler and Brown, 2009), whereas AIF has a time-step of 1 year and a resolution of 40km. Studies which have attempted to produce an accurate depiction of grounding line migration have found that very high resolutions can lead to unacceptably small time-steps (Durand et al., 2009; Pattyn et al., 2012) and high computational expense, limiting their application.

2.4.2 Ice Flow Mechanics

The following section summarises the two key components that simulate ice sheet dynamics within ice sheet models: approximations of the Stokes equations, which determine the stress

23 24

Table 2.1: Characteristics of a range of ice sheet models. Adapted from Bindschadler et al. (2013).

Characteristics IcIES Elmer Ice UMISM ISSM Finite element Finite difference, Finite-element Finite element; arbitrary Numerical method with triangular prisms, Eulerian quadrilaterals Lagrangian Eulerian (ALE) Eulerian H: adaptive H: 20 km Antarctica, H: anisotropic (between (between 1 km Grid (H = horizontal, H: uniform 10 km; 10 km Greenland; 3 km on fast ice streams on the margins and V = vertical) V: 26 layers V: 40 layers, and 15 km in the 70 km in the interior); non-uniformly spaced interior); V: 14 layers V: 17 layers Adaptive, Time-step 1 month 1 year 2 months up to 0.125 years Ice flow Shallow ice Full Stokes Shallow ice Higher order mechanics Weertman sliding. Lubrication Weertman sliding Weertman sliding with Basal sliding factor proportional to Viscous sliding law (m = 3) sub-melt sliding basal water amount Basal water conserved Basal hydrology None None with source calculated None from basal melting No. Melting applied at last Ice shelves No No Yes grounded gridpoint GL moves when surface falls below a flotation height. Ice is cut off Grounding At unbuttressed GL, local Fixed grounding lines when thickness is below Fixed line mass balance is added to & calving front flotation thickness the thinning rate and modified by the Weertman parameter Characteristics PISM PennState3D AIF CISM 2.0 Finite difference, Finite difference, Finite difference, Finite difference, Eulerian, Numerical method Eulerian Eulerian Eulerian mass & tracer advection Grid (H = horizontal, H: 5 km; H: 20 km; H: 40 km average grid spacing; H: uniform 5 km; V = vertical) V: 10 m, equally spaced V: 10 unequal layers V: 20 evenly spaced layers V: 11-level σ coordinate Adaptive, typically Time-step 0.5 - 1 year 1 year 0.1-0.2 year as required ∼15 days Ice flow Hybrid Hybrid Higher-order First-order mechanics Weertman sliding with Weertman sliding with Basal sliding Coulomb plastic Linear viscous sliding law basal temperature dependence basal shear stress relation Basal hydrology Controls bed strength None None None Ice shelves Yes Yes No No GL & calving front allowed to Grounding Sub-grid GL Sub-grid GL GL detected by floating condition. retreat, but not advance past line scheme scheme Margin moves freely present-day margin 25 field across an ice mass and its evolution over time; and Glen’s Flow Law, which describes the flow of ice through internal deformation.

Stokes Approximations

The Navier-Stokes equations are used to model the stresses acting on ice sheets. These differential equations are applied in many areas of science and engineering to calculate linear momentum for an incompressible Newtonian ﬂuid with constant properties (Cengel and Cimbala, 2006). Solving the Stokes equation has been a major challenge for glacier modellers and it is only recently that the techniques and computing power required have been realised (Benn and Evans, 2010; Pattyn, 2008). Most glacier models still simplify the Stokes equations to produce an approximate solution from the higher-order and Blatter (1995) models (Bueler and Brown, 2009). Ice sheet models that use approximations of the Stokes equations offer better computational value but may fail to capture one or more aspects of the ice sheet.

Ice shelf flow is characterised by longitudinal stretching and lateral shearing, whereas grounded ice sheet flow is dominated by horizontal shearing (Durand et al., 2009). Different approximations must be utilised to model the varying flow types. The Shallow Ice or “classical lubrication” Approximation (SIA) is used to calculate flow of the grounded ice sheet, and the Shallow Shelf Approximation (SSA) is used to calculate ice shelf flow. These equations simplify the Stokes equation by using the small depth to width ratio of ice sheets, hence the term “shallow”.

The ﬁrst continental-scale ice sheet models employed the SIA as the sole approximation. This led to unrealistic simulations as all ice was assumed to be frozen to the bed and so these ice sheets were slow to respond to climate changes. Some more recent models, such as IcIES and UMISM are also SIA-only models (see Table 2.1), however, these models are greatly improved by better resolution and grounding line parameterisation.

Many ice sheet models use both the SSA and SIA, to better represent ice streams, which have a plug flow regime, similar to that of ice shelves. These “hybrid” models include the PennState3D model (Pollard and DeConto, 2012) and PISM (Bueler and Brown, 2009). The two approximations can be combined using different methods. Where the SIA and SSA are solved only in distinct regions, there can be a sharp change in flow across the grounding line (Ritz et al., 2001). Alternatively. heuristic shear stress and grounding line velocity corrections may be used to combine the approximations (Pollard and DeConto, 2009). PISM incorporates the flow regimes using a universal approach across the whole ice sheet (Winkelmann et al., 2011). Allowing for better representation of ice streams and consistent treatment of all flow regimes (Figure 2.8).

26 Figure 2.8: A schematic showing how PISM combines the SIA and SSA ﬂow regimes across the whole ice sheet. SSA velocity is shown in red, SIA velocity is in blue, and the total modelled velocity is shown in black (Winkelmann et al., 2011).

Glen’s Flow Law

The Stokes equations are combined with Nye’s Generalisation of Glen’s Flow Law (Glen, 1955; Nye, 1957) to determine ice ﬂow from internal deformation. Glen’s Flow Law relates the strain rate to all stresses acting on the ice, which gives the change in internal deformation over time (Equation 2.1).

ε. = A(T )σn (2.1)

Where ε. and σ are the effective strain and effective stress, respectively, and the exponent n for the power law is chosen to be  so that there is a non-linear reaction to applied stress. T is temperature and A can be considered a measure of the viscosity of the ice. As ice temperature increases, its viscosity decreases, causing deformation rates to rise (Figure 2.9). Since the strain rate is the rate of two lengths, it is a dimensionless quantity which is measured in reciprocal of time.

27 Figure 2.9: Variation of strain rate with temperature at constant stress (Hooke, 2005; Mellor and Testa, 1969).

2.4.3 Basal sliding & bed strength

The sliding law relates basal velocity to basal stress (Weertman, 1957). The majority of ice sheet models use a version of the Weertman sliding law (Weertman, 1957) which best represents hard beds (see Table 2.1). Lliboutry (1959) dismissed Weertman’s sliding law as "inconsistent with facts" and proposed a viscous sliding law across a rough bed. Neither sliding law can be applied to soft beds, which presents a problem as the WAIS has a bed that largely consists of slippery marine sediments. With basal conditions such as this, till rheology must be included in any sliding law.

The Mohr-Coulomb model for saturated till has been found to best represent glacial tills (Tsai et al., 2015). Since melt-water at the bed reduces adhesion, it follows that bed strength is controlled by basal hydrology (Bindschadler et al., 2013). PISM includes a non-conserving hydrology model that is used with the Mohr-Coulomb model. These models and their use with PISM are discussed further in Chapter 3.

2.4.4 Isostatic adjustment

Since isostasy plays an important role in ice sheet dynamics, ice sheet models must be coupled to a lithosphere-asthenosphere model. Le Meur and Huybrechts (1996) reviewed each bedrock

28 treatment and found that although the computationally expensive self-gravitating visco-elastic spherical Earth (SGVE) model was the most accurate, the Elastic Lithosphere Relaxing Asthenosphere (ELRA) model was able to simulate the most important characteristics, while being more suitable in terms of computer requirements. These ﬁndings were supported by Parizek and Alley (2004) who noted that the lithospheric ﬂexural rigidity was more realistic than a simpler local lithosphere model.

The ELRA model of Lingle and Clark (1985) consists of two layers: the lithosphere approximated as an elastic plate of ﬂexural rigidity, which overlies a linearly viscous upper mantle (Bueler et al., 2007). This model was designed to approximate the deformation of the Earth under a single ice stream, and so Bueler et al. (2007) have adapted it for whole-continent scale ice sheet simulations. This Earth model is discussed further in Chapter 3.

2.4.5 Grounding Lines

The grounding line marks the point where grounded ice sheet becomes floating ice shelf. It is a transition between the shear flow of the grounded ice and the longitudinal flow of the floating ice. This boundary is not sharply defined and depends on processes such as basal sliding and sediment deformation. Figure 2.8 illustrates how flow changes across the grounding line.

The grounding line is the only region of the ice sheet where full-Stokes models are signiﬁcantly more accurate than hybrid or SIA models (Larour et al., 2012). Tracking the evolution of a free boundary is complex and so this region presents one of the biggest challenges to ice sheet modellers. Even high-order models are not wholly valid here, however, there are various approaches which can be taken to grounding line simulation (Pattyn et al., 2013).

Within ice sheet models, grounding lines may be fixed or free to evolve. In fixed grid models, the grounding line position is not clearly defined, and simply sits between grid nodes for grounded ice and floating ice. Some fixed grid models, such as those with irregular or anisotropic horizontal grids, have smaller grid squares around the grounding line so that advances and retreats may be modelled at sub-grid level (Pattyn et al., 2013).

For moving-grid models, the grounding line is ﬁxed to grid nodes so that it can be tracked as the ice sheet evolves using a stretched coordinate system. Hybrid models use heuristic superpositions of depth-integrated SIA and SSA equations to model grounding line migration. Although this approach is valid over timescales greater than 100 years (Pattyn et al., 2013), higher-order (Blatter-Pattyn) and Full-Stokes models are more suitable for shorter-term projections (Pollard and DeConto, 2012).

A grid size of at least 100m is required for a realistic representation of grounding line movement, however this is prohibitively computationally expensive for whole ice sheet simulations. One

29 approach to this problem is an adaptive-mesh, where grounding lines are modelled at a higher resolution than the rest of the ice sheet, but this remains computationally expensive (Cornford et al., 2013). An alternative approach is to to determine grounding line position at a sub-grid scale (Gladstone et al., 2010).

2.4.6 Climate and surface mass balance

Ice sheet models typically use present day climatology inputs with proxy data to calculate time-series perturbations, or can be online or ofﬂine coupled to a general circulation model. Net mass balance may be calculated from temperature and precipitation inputs using a positive degree-day scheme (PDD), or from net radiation and heat ﬂuxes at the ice surface using a full surface energy balance model.

The positive degree-day algorithm is a simple, non-deterministic technique for expressing surface mass balance (SMB). It is useful for long-term SMB and can be applied where there is limited spatial and temporal SMB data, as well as on longer timescales where PDD can be more effective than data (Huybrechts and Oerlemans, 1990).

The surface energy balance model uses a physically based approach to calculate melt from the energy ﬂuxes to and from the ice surface. Although this model generates more accurate simulations than the PDD model, it has a large input data requirement that includes cloud cover, surface albedo, wind speed, relative humidity, and more (Hock, 2005). The surface energy balance model may be a good choice for modelling extensively studied valley glaciers, but the PDD model is the most commonly used for ice sheet modelling studies, particularly for Antarctica, where data is sparse.

Ice sheet models and GCMs may be coupled one-way or two-way. The former involves deriving SMB and temperature ﬁelds from the GCM and then driving the ice sheet model off-line, whereas the latter allows changes in the ice ﬂow to drive changes in the GCM (Fischer et al., 2014). Although one-way coupling excludes feedbacks from the ice sheet to climate, useful insights have been gained through this method with less demand made on computer resources (Leysinger-Vieli and Gudmundsson, 2004; Benn and Evans, 2010). The ideal solution is to directly couple the ice sheet model and GCM, with both environments evolving in real time, however this is computationally demanding.

2.5 Modelling past and future ice sheet contribution to sea level rise

Ice sheet models have been used to model ice sheet dynamics and change in response to both past and possible future climate scenarios. Some recent modelling studies have attempted to

30 predict Antarctica’s contribution to GMSL in the coming centuries, with diverse results (Ritz et al., 2015; Golledge et al., 2015; DeConto and Pollard, 2016). Ritz et al. (2015) suggest that Antarctica will contribute ∼30 cm sea-level equivalent by 2100 under the old mid-range scenario A1B, which is closest to RCP6.0, whereas DeConto and Pollard (2016) predict that Antarctica will contribute 32 cm sea-level equivalent by 2100 for the more optimistic RCP4.5 emissions scenario.

The Ritz et al. (2015) study uses the GRISLI (GRenoble Ice Shelves and Land Ice) model, which is a 3D ﬁnite difference thermomechanically coupled hybrid ice sheet model, with a focus on introducing probabilities and statistical factors into ice sheet modelling. Experiments were run at a 15 km horizontal resolution over 200 years, from 2000 to 2200, driven by the HadCM3 general circulation model scenario A1B, which is closest to RCP6.0.

Using the above setup, Ritz et al. (2015) applied probability distributions of retreat to 11 different sectors, based on observed grounding line retreat in the Amundsen Sea embayment. Each drainage basin was assumed to be independent. Grounding line retreat was only permitted over bedrock that is below sea level and down-sloping inland, and ice shelf fronts were maintained in their present positions. No calving law was applied, instead the model uses a “horizontal wastage rate”, which only applies to cliffs with a surface elevation 100 m above sea level. Ice retreat in areas of shallow bedrock was limited by a “no suction check”. This parameter is the maximum stress that is theoretically permissible at the grounding line, based on the free-water tensile stress. When this “no suction check” is switched off, the ice sheets contribution to SLR by 2200 increased by 52%, from 152 to 230 cm.

Only downslope grounding line retreat was permitted, present ice shelf fronts were maintained and the “no suction check” all limited ice sheet melt. Furthermore, retreat probabilities were based on present observations and the study does not make allowances for increasing retreat rates as the ice sheet evolves. Furthermore, the model was calibrated to laser altimetry measurements, rather than GRACE mass measurements, which give a lower estimate of ice loss.

Although the above is arguably a thorough study, with 3000 model versions, the limits imposed mean that results are unlikely to reﬂect long-term changes and give conservative estimates for short-term marine ice sheet instability (MISI).

The DeConto and Pollard (2016) model (PennState3D) is also a hybrid model, with similarities to GRISLI. In the DeConto and Pollard (2016) study, the future scenario simulations begin in 1950 and run for 550 years to 2500 at a resolution of 10 km. Ocean temperatures are provided by high-resolution (0.5◦ atmosphere and 1◦ ocean) NCAR CCSM4 ocean model output, following the RCP2.6, RCP4.5, and RCP8.5 greenhouse gas emissions scenarios run to 2300. Surface mass balance is provided by decadal averages of meteorological ﬁelds from the RegCM3 RCM, nested within the GENESIS v3 global climate model. Basal heat ﬂux is represented by a basic

31 two-value pattern: 54.6 mW/m2 under EAIS and 70 mW/m2 under WAIS.

Following work by Bassis and Jacobs (2013) and Pollard et al. (2015) which sought to improve the representation of different calving mechanisms within ice sheet models, DeConto and Pollard (2016) introduced Bassis fracture equations to their model. Here, ice is treated as a granular material made of boulders of ice that are bonded together. When the shear or tensile stress exceeds a threshold of 1 MPa or 0.1 MPa respectively, these bonds break, leading to fracturing. Cliff failure depends on the depths of these crevasses, relative to total ice thickness.

Since ice is permitted to retreat uphill and ice shelf fronts are not maintained at their present locations, this study produces dramatically different results to the Ritz study; predicting that Antarctica will contribute 32 cm sea-level equivalent by 2100 for the optimistic RCP4.5, which is 2 cm greater than the Ritz et al. (2015) result for the A1B Special Report on Emissions Scenario, which is broadly equivalent to RCP6.0. Under the RCP8.5 scenario, this rises to 12.3 m sea-level equivalent by 2500 (DeConto and Pollard, 2016).

As the DeConto and Pollard (2016) study is only a preliminary attempt at improving calving in ice sheet models, there are many uncertainties, which need to be resolved. The parameterisations for cliff failure and melt-driven hydrofracture contain poorly-constrained coefﬁcients. This is clearly demonstrated by the fact that the ice sheet model can contribute ∼17 m sea-level equivalent by surface warming and enhanced calving alone. This is extremely high mass loss from only limited forcing. Unresolved issues such as whether hydrofracturing weakens ice columns at the grounding line, and the importance of the small-scale pinning of ice shelves are highlighted by Pollard et al. (2015).

An alternative approach was followed by Golledge et al. (2015), who used the hybrid Parallel Ice Sheet Model (PISM) to simulate the millennial-scale response of the Antarctic ice sheets to the four representative concentration pathways (RCPs) from the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC, 2013). Each experiment was run for 5000 years, with environmental parameters evolving as indicated in the extended CMIP5 RCP ensemble mean trends. Rather than using a basic two-value grid of geothermal heatﬂux values, this study used the ALBMAP compilation (Le Brocq et al., 2011) of the Fox-Maule et al. (2005) and Shapiro, N.M. and Ritzwoller, M.H. (2004) datasets. High and a low estimates were given as PISM includes an optional component that interpolates basal melt ﬁelds at the grounding line (Feldmann et al., 2014) and it is uncertain whether this mechanism leads to an ice sheet that is oversensitive to perturbations.

Initial ice sheet response was relatively conservative, with simulations giving a 1 to 19 cm contribution to SLR by 2100 CE under RCP4.5, however the ice sheet was found to be committed to greater mass loss over the subsequent millennia. At 2300 CE, the RCP4.5 scenario results in SLR of 61 to 95 cm, and by 5000 CE, this has reached 277 to 430 cm (Golledge et al.,

32 2015). The RCP8.5 scenario gave 10 to 39 cm SLR at 2100 CE, 160 to 296 cm at 2300 CE, and 520 to 931 cm at 5000 CE. These are more modest results than those found in the study by DeConto and Pollard (2016), which simulated 12.3 m SLR by 2500 under the RCP8.5 scenario.

The use of ensemble mean climate forcing could explain the smaller response implied by the PISM study of Golledge et al. (2015) as this does not fully account for polar ampliﬁcation, which can lead to ocean and surface air temperatures that are substantially larger than the magnitude of the mean. Another factor which could lead to reduced mass loss is that a coupled climate model was not used, and so feedbacks such as ocean circulation changes, are not captured. Margin retreat may be underestimated in some areas as local rises in sea level due to mass loss from distal sectors of the ice sheet are not included in the simulations (Golledge et al., 2015). However, the latter two of these factors will also be an issue in the DeConto and Pollard (2016) study.

Ideally, simulations of future sea level rise are more reliable when constrained by observations of past changes. Fyke et al. (2011) used an earlier version of the PennState ice model to simulate the LIG Antarctic ice sheets. The early model did not include the cliff failure parameterisations and they found no signiﬁcant change in Antarctic ice volume under a LIG climate, which contrasted with the palaeo-record. With time-evolving atmospheric and oceanic climatologies and the cliff failure parameterisations included, 6–7.5 m SLE of mass is lost from the Antarctic ice sheets during the LIG (DeConto and Pollard, 2016).

External climate inputs have been identiﬁed as one of the largest sources of uncertainty in ice sheet modelling (Levermann et al., 2013). Uncertainty in the proxy sea surface temperature estimates used by DeConto and Pollard (2016) are >2◦C. Sutter et al. (2016) force their ice sheet model using COSMOS GCM outputs for the LIG in a similar setup to Lunt et al. (2013). Uniform ocean temperature anomalies of 1 to 3◦C above present day were applied to test tipping points for WAIS collapse. They found that the anomaly threshold for WAIS collapse and 3-4 m SLR during the LIG is 2 to 3◦C.

Chapter 3

Methods

This chapter will give an overview of the ice sheet model chosen, as well as the models that provided the climate perturbations, and the datasets that were used for other boundary conditions. The experiment set up is explained and the choice of parameter settings are justiﬁed.

3.1 Model description

3.1.1 Parallel Ice Sheet Model (PISM)

The Parallel Ice Sheet Model (PISM) version 0.7 was the ice-sheet model chosen for this study (Bueler and Brown, 2009). It is a hybrid, three-dimensional, thermodynamic, coupled ice sheet-ice shelf model. It solves the shallow ice and shallow shelf approximations to simulate the ice sheet, including ice streams and ice shelves. The model also uses a sub-grid interpolation scheme at the grounding-line, which allows realistic simulation of grounding-line migration without the computational expense of running whole ice sheet experiments at the high-resolutions that would otherwise be required. Feldmann et al. (2014) found that PISM’s performance is comparable to Elmer/Ice’s, but PISM is twice as fast.

The ice sheet model required for this study needed to be capable of running at a high spatial resolution, since models with a poor spatial resolution are unable to represent ice streams, but it must also be computationally efﬁcient enough to be run over a long time-span. The most suitable method of ice ﬂow mechanics for a study of this kind is a hybrid shallow ice and ‘shelfy’ stream model, which should produce a valid result within the computing restraints. PISM was chosen for this study because it is fast, computationally inexpensive, and has been shown to be an effective model for multi-millennial simulations of the Antarctic ice sheets (Golledge et al., 2015, 2017a,b). A detailed description of the model setup and parameter settings can be found in Section 3.4.

35 3.2 CSIRO Mk3L

Anomalies for the interglacial climate were generated by Steven Phipps especially for this project using the CSIRO Mk3L climate system model, hereafter referred to as Mk3L. Mk3L is an intermediate-resolution general circulation model (GCM) developed for use in palaeoclimate studies (Phipps et al., 2011). It is made up of a fully coupled spectral GCM, a thermodynamic sea ice model, a static vegetation land surface scheme, and an ocean GCM. It is capable of computationally efﬁcient millennial-scale simulations of climate variability and change.

There are 18 vertical levels in the atmosphere model, with a zonal resolution of 5.625◦, and a meridional resolution of ∼3.18◦. Ice sheets are not represented in Mk3L, however perennial ice cover is reﬂected in the land surface and soil types. The land surface scheme also includes a snow model which calculates snow albedo and the snow density and thickness of three snowpack layers (Phipps et al., 2011).

The ocean model is a coarse resolution GCM which uses the rigid-lid approximation of the Navier-Stokes equations (Cox, 1984). The rigid-lid approximation assumes that there are no height perturbations and removes the unnecessary complication of high-speed gravity wave effects. The ocean GCM (OGCM) has 21 vertical levels to a depth of 4600 m (Phipps et al., 2011).

The sea-ice model uses cavitating ﬂuid rheology to simulate internal stresses. Ice grows when the water temperature falls below freezing point (-1.85◦C). Ice advection is driven by wind stress and ocean currents taken from the other components of the climate system model. Sub-grid features such as leads and polynyas can be represented through fractional sea ice cover within grid points (Phipps et al., 2011).

The coupling between the atmosphere-land-sea-ice model (AGCM) and the OGCM is frequent in comparison to other climate system models. Many other climate system models exchange fields just once a day, whereas Mk3L exchanges fields every hour. This allows better resolved diurnal cycles in SST and salinity. The OGCM fields that are passed on to the AGCM are: the zonal and meridional components of the surface velocity, sea surface temperature, and sea surface salinity. The AGCM fields that are passed on to the OGCM are: the zonal and meridional components of the surface momentum flux, and the surface heat and salt fluxes (Phipps et al., 2011).

Although its reduced spatial resolution can cause some regional scale biases, Mk3L does accurately simulate large-scale features (Phipps et al., 2011) (Figure 3.1). Mao et al. (2011) state that, while some higher-order models are better at simulating observed climate, they would be unsuited to multi-millennial experiments such as this one, due to computational expense.

36 Figure 3.1: “Error” in the Mk3L pre-industrial control simulation for surface air temperature. From Lunt et al. (2013).

In common with other GCMs, Mk3L has been shown to slightly underestimate Antarctic air temperatures (Figure 3.1) however, limited observations at high latitudes mean that there are uncertainties in this error calculation (Lunt et al., 2013; Fischer et al., 2018). The model uses ﬂux adjustments for surface heat and surface salinity which lead to lower errors over the ocean than other GCMs. These ﬂux adjustments are especially important over millennial time scales (Phipps et al., 2012; Lunt et al., 2013).

Mk3L is well suited to this investigation as it was speciﬁcally designed for multi-millennial palaeoclimate studies (Phipps et al., 2012, 2013). It can adequately simulate climates on a continental scale, providing the four inputs (surface air temperature, precipitation, ocean temperature and ocean salinity) needed to drive the ice-sheet model within the time constraints of this study.

3.3 Ice sheet model inputs

PISM was driven by two atmosphere and two ocean variables: surface air temperature and precipitation, and sea surface temperature and ocean salinity.

Present day surface air temperature and precipitation data from the regional climate model (RCM) RACMO 2.3 (van Wessem et al., 2014) and a grid of predetermined values were prescribed, respectively, as the atmosphere and ocean inputs for the ice sheet model tuning experiments.

37 For the LIG forcing experiments, anomalies from a general circulation model (GCM) were applied to the RACMO 2.3 dataset and ocean grid using a lapse rate modiﬁer (Figure 3.2). The CSIRO Mk3L intermediate-resolution GCM (Phipps et al., 2011, 2012) was used to generate these climate anomalies.

Figure 3.2: PISM climate input data ﬂow. From the PISM climate forcing components handbook.

This section contains an overview of the atmosphere and ocean boundary conditions used and the models that generated them.

3.3.1 Tuning experiment inputs

Ocean

Typically, sea surface temperatures that are in contact with the ice front average freezing for most of the year (Schmidtko et al., 2014). Therefore, a grid of values set to freezing point (-1.8◦C) was prescribed for sea surface temperature, following the method of Golledge et al. (2015).

38 Antarctic ocean salinity tends to average ∼34.5 g/kg, however this value generates non-viable levels of melt in the simulated ice sheet. In order to determine the best salinity value to apply, basic sensitivity testing was carried out using out-of-the-box parameter settings. A value of 24.5 g/kg was found to be the most appropriate based on the accuracy of grounding line simulation, particularly around the Weddell Sea (Figure 3.3).

Figure 3.3: Out-of-the-box parameter setting were used in a series of sensitivity tests to determine the control value for salinity. Black lines represent the starting position of the grounding lines and calving fronts, pink lines represent the modelled grounding lines and calving fronts. A: 24 g/kg, B: 24.5 g/kg C: 25 g/kg.

A value of 24.5 g/kg also conserved mass better than the other values (Figure 3.4), with a mean ice thickness of 809.5 m. Salinities of 24 g/kg and 25 g/kg were only able to achieve mean ice thicknesses of 785.7 m and 781.7 m, respectively.

Figure 3.4: Ice thickness differences for the out-of-the-box sensitivity tests used to determine the control value for salinity. A: 24 g/kg, B: 24.5 g/kg C:25 g/kg.

Atmosphere

Present day surface air temperature and precipitation data from the regional climate model (RCM) RACMO 2.3 was used to drive the model in the tuning experiments (Figure 3.5). Anomalies from a GCM were then applied to this dataset for the forced LIG experiments.

39 The Regional Atmospheric Climate Model or RACMO2.3, was created by the Royal Netherlands Meteorological Institute (KNMI) (van Meijgaard et al., 2008). It is a regional climate model that has been adapted for use in polar regions. This simulates SMB, air temperature and precipitation data over the period 1979–2011 for the Antarctic ice sheet, with small inter-annual variability.

Although the model cannot resolve steep topography, leading to wind speed biases (van Wessem et al., 2014), it was chosen for this investigation because it generates a more realistic snow accumulation dataset for the Antarctic interior than other RCMs (Wang et al., 2016).

40 (a)

(b)

Figure 3.5: Present day atmospheric data from the RACMO 2.3 RCM was used for model tuning and climate anomalies were applied to this dataset for the forcing experiments. (a) Surface air temperature (b) Precipitation

41 3.3.2 Climate anomalies

Climate anomalies from the Mk3L climate system model were applied to the RACMO2.3 present day climate data used in the model tuning experiments.

Climate experiment setup

The model setup for the LIG experiments was the same as the PMIP3 model setups (Lunt et al., 2013). See Table 3.1 for details of the orbital parameters used.

Table 3.1: Orbital parameters (Lunt et al., 2013).

Year Eccentricity Obliquity (◦) Perihelion (◦) 130ka 0.038209 24.242 228.32 128ka 0.039017 24.131 259.65 125ka 0.040013 23.798 307.14 115ka 0.041421 22.405 110.88

Where σ represents standard deviation, σ0 is the standard PMIP3 experiment for the LIG. Peak

CO2 then increases for each experiment by σ2, σ4, σ6, σ8 and σ10 (Table 3.3). This encompasses the upper bound of the ice core trace gas records, at σ4 (Köhler et al., 2017) and slightly beyond.

All greenhouse gas emissions are converted into CO2 equivalent values. Over 100 years, 1 kg of

CH4 has the equivalent warming potential of 25 kg of CO2, and 1 kg of N2O has the equivalent of 298 kg of CO2.

Table 3.2: Peak LIG CO2 levels from ice core records.

Peak CO Ice core 2 (ppm) Vostok 287 (Petit et al., 1999) Dome Fuji 282 (Watanabe et al., 2003) EPICA Dome C 298 (Köhler et al., 2017) EPICA Dome C 290 (Lourantou et al., 2010) TALDICE 294 (Masson-Delmotte et al., 2011)

42 Table 3.3: Peak CO2, peak CO2e, mean CO2 and mean CO2e levels for each experiment. Peak CO2 is reached at 128.4 ka for each experiment. The mean is calculated for the period 130 to 125 ka.

Peak CO Peak CO e* Mean CO Mean CO e* Experiment 2 2 2 2 (ppm) (ppm) (ppm) (ppm) σ0 287.1 284.1 271.6 265.4 σ2 291.1 291.5 275.6 271.9 σ4 295.1 298.2 279.6 278.4 σ6 299.1 304.9 283.6 284.9 σ8 303.1 311.7 287.6 291.6 σ10 307.1 318.5 291.6 298.2 *GHG equivalent

The GCM simulation was run for 132 to 115 kyrs BP. Since a 17,000 year simulation is unfeasible, the model run was accelerated by a factor of 10 using the Lorenz and Lohmann (2004) acceleration technique, so the LIG is represented by 1700 model years. This acceleration technique can produce errors if used over long periods of time, such as full glacial cycles, however the impact on a study of this length is negligible (Phipps et al., 2012). Anomalies were calculated by averaging the outputs for the period 130 to 125 ka, which incorporates peak CO2 at 128.4 ka, and subtracting this from the pre-industrial control. These anomalies were used as inputs for the ice sheet model.

The Mk3L ocean outputs were interpolated using tension spline interpolation. Although this method introduces uncertainties to the dataset, applying a uniform value beneath the starting ice-shelf and ice sheet conﬁguration was found to generate unrealistically conservative results (see Appendix A).

As sea surface temperatures were found to reﬂect the much cooler surface air temperature and produce unrealistic results (see AppendixB), mid-depths (400-700 m) ocean temperatures and salinity anomalies were used. This captures the warmer ocean temperatures. These values were interpolated and applied to the grids of freezing-point values.

Atmosphere inputs

Constant climate forcing was used since climate inputs covering a glacial cycle before the LIG are not available and so transient forcing could not be implemented as the ice sheet needs to evolve from glacial to LIG to be a valid simulation. Previous studies have applied a transient LIG climate to present day or Last Glacial Maximum (LGM) ice sheet geometry (DeConto and Pollard, 2016; Sutter et al., 2016), however, this is a physically unsound approach that gives a false rate of mass loss. Although constant climate forcing does not give a rate of change, it does give a good indication of potential mass loss under a prescribed climate, and so this method was chosen. The GCM air temperature and precipitation outputs were applied using an anomalies modiﬁer.

43 Two-way climate-ice-sheet model coupling was deemed too computationally expensive for this study, and so a one-way, ofﬂine approach was used. Although one-way coupling has limitations, it is suitable for an investigation into ﬁrst-order ice sheet responses to climate perturbations (DeConto and Pollard, 2016; Sutter et al., 2016; Golledge et al., 2017a).

Since the ice sheet is not fully coupled to a GCM, a positive degree day (PDD) model is used to adjust surface air temperature and precipitation as the ice sheet elevation changes. The PDD model is based on Calov and Greve (2005). It converts near-surface air temperature and precipitation to top-of-the-ice temperature and surface mass balance. When air temperatures are above 2◦C, the scheme interprets all precipitation as rain, and when air temperatures are below 0◦C, all precipitation is interpreted as snow. A lapse rate given in K/km of elevation change corrects for elevation changes. In line with previous studies, a constant lapse rate of 8 K/km per day is applied across the ice sheets (Rutt et al., 2009; Golledge et al., 2015; DeConto and Pollard, 2016).

Ocean inputs

PISM is currently unable to accept a full ocean temperature proﬁle and so the impact that varying temperatures at different depths has on the ice sheets cannot be investigated. However, the basal melt interpolation function does allow for the effects of greater pressure at depth (see Section 3.4.3).

Sub-shelf ice temperature and sub-shelf mass ﬂux are calculated using the thermodynamic ice-ocean model (Holland and Jenkins, 1999). This model takes the interior properties of the ice shelf and the properties of the ocean surface to calculate basal shelf melt rates. It adopts the three equation approach proposed by Hellmer and Olbers (1989), which uses temperature, salinity, and pressure to determine freezing point dependence, heat conservation and salt conservation in the boundary layer.

3.3.3 Other boundary conditions

Initial ice thickness & basal topography

The mapping of Antarctica’s basal topography is a huge task, with data being acquired by many researchers from many different countries over many years. The original Bedmap compilation was released in 2001 (Lythe et al., 2001) and was made up of measurements collected over 50 years. Some new data was added and new interpolation schemes were applied by Le Brocq et al. (2011) as part of ALBMAP, before the release of Bedmap2 in 2013 (Fretwell et al., 2013).

Sensitivity tests were run, using out-of-the-box parameter settings, to assess the impact on PISM outputs of updating the ice thickness and basal topography dataset from ALBMAP to Bedmap2. The grounding-line positions are very similar for both experiments, however, the detail included

44 in the Bedmap2 dataset, such as the deep trough beneath the Recovery Ice Stream, has led to improved ice thickness simulation (Figure 3.7a). Bedmap2 (Fretwell et al., 2013) was chosen as the dataset for the initial ice thickness and basal topography.

Geothermal heat ﬂux

Antarctic geothermal heat flux is a crucial boundary condition. It causes ice flow to increase through two mechanisms: softening of the basal ice, and contributing to the supply of melt water at the bed. The main source of geothermal heat is flux from the mantle, however, heat production in the crust due to radioactive decay and tectonic history are also important (Pollack et al., 1993; Martos et al., 2017).

Measuring geothermal heat ﬂow beneath the Antarctic ice sheets is extremely challenging and so studies tend to rely on modelling and satellite magnetic data (Shapiro, N.M. and Ritzwoller, M.H., 2004; Fox-Maule et al., 2005). Some studies have used seismological data (An et al., 2015), but due to the difﬁcult environment there are few seismic stations in Antarctica.

There were three Antarctic geothermal heat flux data sets available for this study: Shapiro, N.M. and Ritzwoller, M.H. (2004); Fox-Maule et al. (2005) and An et al. (2015). These datasets largely with conflict one another, although they do all show greater heat flux beneath the WAIS than beneath the EAIS. The Shapiro, N.M. and Ritzwoller, M.H. (2004) dataset has much higher heat flux values for the WAIS than both Fox-Maule et al. (2005) and An et al. (2015). The Fox-Maule et al. (2005) dataset has a higher heat flux for the EAIS than the other studies.

With little confidence in any of the studies due to their conflicting results, the most recent (An et al., 2015) was used as this study included data from seismographs, as well as modelling. A sensitivity test was performed to compare the different impacts of the An et al. (2015) geothermal heat flux data and the Shapiro, N.M. and Ritzwoller, M.H. (2004) geothermal heat flux data on the ice sheet. The differences are largely seen in the WAIS, where the Weddell Sea, Amundsen Sea and the West AP grounding lines all retreat further when the An et al. (2015) dataset is used (Figure 3.6b).

45 (a)

(b)

Figure 3.6: Difference in ice thickness between (a) ALBMAP (Le Brocq et al., 2011) and Bedmap2 (Fretwell et al., 2013) topography data sets, and (b) the (Shapiro, N.M. and Ritzwoller, M.H., 2004) heat ﬂux dataset and the (An et al., 2015) dataset. The ALBMAP and Shapiro, N.M. and Ritzwoller, M.H. (2004) grounding lines are shown in black, and the Bedmap2 and An et al. (2015) grounding lines are shown in grey.

46 3.4 Ice sheet model parameters

PISM has over 200 user-conﬁgurable parameters. Some of the parameters do not need adjusting from the default PISM setting, but others need tuning according to the requirements of the study. For example, a study focusing on the EAIS will need different parameter settings to a study looking at the WAIS, due to the different physical characteristics of the two ice sheets. This study is investigating both east and west Antarctica, as well as the Peninsula, which makes parameter tuning particularly challenging.

The key parameters are: the enhancement factors, yield stress, the grounding line, calving, bed deformation, the PDD model and the lapse rate.

3.4.1 Enhancement factors

The Shallow Ice Approximation (SIA) reduces the Stokes equations from a three-dimensional problem in four variables to a two-dimensional problem in thickness (Hutter, 1983; Hindmarsh, 2004). The SIA neglects the transverse and longitudinal strain rate components and balances gravitational driving stress with shear stresses (Huybrechts, 2009). Although the SIA represents the ﬂow of non-streaming regions of ice well, the ﬂow of ice streams and ice divides is poorly represented since the accuracy of SIA decreases as the importance of basal slip to glacier motion increases (Schoof, 2006; Gudmundsson, 2003).

The Shallow Shelf Approximation (SSA) is the simplest form of a membrane stress balance which can be obtained from the Stokes model (Weis et al., 1999; Bueler and Brown, 2009). It disregards vertical shear stresses and makes the assumption that horizontal velocity is independent of depth (Weis et al., 1999; Larour et al., 2012; MacAyeal, 1989; Morland and Zainuddin, 1987). Parameterisations for plastic and linear till can be added to better represent the ﬂow of ice streams and ice divides.

Ice streams have a plug ﬂow regime, similar to that of ice shelves (horizontal velocities are constant over depth), but with the addition of basal resistance. Hybrid models, such as PISM, make use of both the SSA and the SIA to better represent ice streams.

3.4.2 Yield stress

Experimental evidence has found that glacial tills are best described by Coulomb plastic rheology, where basal shear stress is proportional to the effective pressure (Tsai et al., 2015). The Mohr-Coulomb criterion for yield stress is calculated with till cohesion, till strength and effective pressure. Effective pressure is dependent on the amount of water in the till and the ice overburden pressure. The default values are based on laboratory experiments (Tulaczyk et al.,

47 2000). Till cohesion is set to 0 as cohesion is negligible for deformable beds (Cuffey and Paterson, 2010).

Till strength is given as the tangent of a friction angle. Friction angle values tend to be between 10◦ and 40◦. Clay-rich tills have a till fraction angle of <20◦, whereas angles for sandy tills are usually >20◦ (Cuffey and Paterson, 2010). Till strength within PISM changes with bed elevation. This allows the representation of weak marine sediments found at low elevations.

The Mohr-Coulomb model is combined with the null subglacial hydrology model, which is non-conserving and stores water in the till up to a maximum of 2 m saturation thickness. Water is lost permanently once this 2 m threshold is exceeded.

3.4.3 Grounding-line

The "LI" grounding-line component was used, which is a 2D sub-grid scheme developed by Gladstone et al. (2010) that interpolates basal shear stress. This scheme allows the simulation of grounding-line behaviour for multi-millenial, continental-scale experiments, without the problems of computational expense and time constraints presented by high-resolution, full-Stokes simulations.

This component includes an optional fractional ﬂoatation mask that interpolates basal melt at the grounding line, representing the intrusion of water beneath the ice sheet. This basal melt interpolation function allows for the enhanced basal melt at the grounding line due to the pressure effects of deeper bathymetry. The implementation of this function accelerates mass loss, but not to the same extent as the cliff-collapse mechanism of Pollard et al. (2015). It is unclear whether this function leads to a more or less realistic simulation, however, the mechanism corresponds with current grounding line behaviour (Gladstone et al., 2017). Experiments were run with the basal melt interpolation switched off to produce "low-melt" scenarios, and switched on to produce "high-melt" scenarios.

Both scenarios include a heuristic scheme that reduces the basal traction immediately upstream of the grounding line to smooth the transition between floating and grounded ice. It is possible that this component could make the ice sheet over-sensitive to perturbations, however, previous studies have shown that its implementation leads to only minor change relative to not using the parameterisation (Golledge et al., 2015). Using this scheme is more computationally efficient than a high-resolution transition zone, but generates a more realistic simulation than SSA alone, without the floatation criterion.

48 3.4.4 Calving

A sub-grid scheme is also implemented at the calving front (Albrecht et al., 2011). This parameterisation relates the ratio of ice coverage to the mean ice-shelf thickness in the adjacent grid square and ensures realistic spreading rates at the ice edge (Albrecht et al., 2011). Without it, ice would spread to ﬁll a whole grid square, resulting in ice shelves less than a few meters thick (Winkelmann et al., 2011).

Observations show that narrower ice shelves have slower calving rates, and high along-ﬂow velocities in wide embayments lead to greater calving rates (Alley et al., 2008). In PISM, this is reﬂected in the eigen-calving function introduced by PISM-PIK (Levermann et al., 2012). This is derived from SSA velocities and is highly dependent on coastal geometry and basal topography at the grounding line.

As well as the eigen-calving function, calving rates are controlled by ice thickness at the calving front. Any ice that is thinner than a prescribed thickness is removed. Since calving fronts of ice shelves are rarely thinner than 150-250 m, a value in this range is usually chosen. In this study, a minimum calving front thickness of 220 m was applied following model tuning experiments.

3.4.5 Bed deformation

This study uses and adaption of the Lingle and Clark (1985) earth deformation model by Bueler et al. (2007), which is an improvement of the ﬂat earth Elastic Lithosphere Relaxing Asthenosphere (ELRA) model (Greve, 2001). The Bueler et al. (2007) uplift model adds mode-dependent relaxation times and elastic deformation of the spherical, layered, self-gravitating Earth to the ELRA model, which uses a linear differential equation to calculate rate of uplift. The new components lead to more realistic simulations, but the updated model has a similar level of computational expense as the original.

3.5 Experiment Setup

There are three stages to each model run: initial smoothing, intermediate, and ﬁnal evolution. A resolution of 20 km was used as this gives good results, without large computational expense (Golledge et al., 2015).

3.5.1 Initial smoothing stage

Classical shallow ice theory cannot incorporate local topography; it is assumed that basal topography is comparable to the ice surface. To overcome this problem, PISM uses Schoof’s mathematical technique (Schoof, 2003), which smooths the bed to 10km, improving the SIA handling of bed roughness as well as the efﬁciency of the model. Bed smoothing is more

49 important for resolutions lower than 20 km, however, model performance is improved at all resolutions and so it has been implemented in this study.

Schoof’s method is applied during a short (100 year) run, forced with a present day climate, with ﬁxed calving fronts. The following intermediate stage is initialised using the output.

3.5.2 Intermediate stage

The intermediate or "no mass" stage is a long, 200,000 year run that allows ice enthalpy and the temperature gradient to evolve, but keeps ice sheet geometry constant. This brings ice temperatures to equilibrium with the prescribed climate inputs.

3.5.3 Final evolution stage

In this ﬁnal stage, the ice sheet evolves for 20,000 years, until it reaches a steady-state. During this run, the ice surface elevation, grounding line and calving line are allowed to adjust to the prescribed boundary conditions and the bed can deform, depress and rebound in response to ice sheet change.

At this stage, the present day climate perturbations are applied for the model tuning runs, and the climate anomalies are applied for the LIG experiments.

3.6 Model tuning

Sensitivity testing was carried out to test parameter settings. Present day climate inputs were provided by RACMO 2.3, and sea surface temperature and salinity were set to freezing point. The ice sheet was allowed to evolve for 20,000 years, until it reached a steady-state.

Rather than take a large-ensemble approach to model tuning, where thousands of low-resolution experiments are conducted in an unguided manner, the targeted approach adopted by Golledge et al. (2017b), was used to determine parameter settings. This involves incrementally reﬁning parameter settings through an iterative process in which the model is constrained to simulate the present-day ice sheet geometry from Bedmap2 (Fretwell et al., 2013), and the Measures surface velocity ﬁeld (Rignot, 2006).

A matrix of values was used to reﬁne the enhancement factors and yield stress parameters. The targeted SIA values covered a range of 2-4, the SSA values tested range from 0.5 to 1.25, φ min friction angles of 10◦ to 20◦ were tested, while the φ max angle did not require adjustment and remained at 40◦. A total of 66 tuning experiments were carried out, and all of the results for these tests can be found in Appendix C.

50 All perturbation experiments start from the this present-day ice sheet initialisation.

3.6.1 Chosen parameter settings

The parameter setup (Table 3.4) gave a mean ice thickness difference between modelled and observed ice sheets of -0.06 m across the whole continent. This difference is not uniform across the ice sheets, with some areas much thicker than observed, and other areas much thinner (Figure 3.7a). The majority of the EAIS interior is 200 to 600 m thinner than observed at present, and much of the AP and Ross Sea sector of the WAIS is over 800 m thicker than observed at present. However, the model reproduces present day grounding and calving lines extremely well, as well as the mean ice thickness difference.

Ice velocities are difﬁcult to accurately simulate across the whole of Antarctica. Modelled mean surface velocities are -63.8 m a−1 slower than observed, with greater than observed velocities in parts of the EAIS, and slower than observed velocities across much of the WAIS (Figure 3.7b).

Table 3.4 shows how these parameters compare to those of previous studies that have used PISM. The yield stress values (φ min and φ max, topo min and topo max) are the same as those applied by Golledge et al. (2015). The study by Pittard et al. (2016) focuses entirely on the Amery sector of the EAIS and so the yield stress values have a much higher range than this study and Golledge et al. (2015). This illustrates how areas with different physical characteristics require different parameter settings. Although smaller-scale experiments allow more precise tailoring of parameter settings, they are limited as they do not capture the dynamics of the whole ice sheet and the interaction between catchments.

As the difference between modelled and observed mean ice thickness is relatively small, and the grounding and calving lines are well reproduced, the results generated from this model setup is considered robust.

Table 3.4: How the parameter settings chosen for this study compare with those applied to previous studies using PISM. SIA is Shallow Ice Approximation, SSA is Shallow Shelf Approximation. Topo min and Topo max are the elevations between which the φ min and φ max values are applied.

Golledge et al. Pittard et al. Parameter This study (2015) (2016) SIA 3.5 1.2 3.0 SSA 0.75 0.5 0.6 φ min (◦) 15 15 15 φ max (◦) 40 40 40 Topo min (m) -300 -300 -2000 Topo max (m) 700 700 4500

51 (a)

(b)

Figure 3.7: (a) Ice thickness differences between modelled and observed ice sheets. Observed grounding lines and calving lines are represented by the black lines, and modelled grounding lines and calving lines are in grey. (b) Surface velocity differences between modelled and observed ice sheets.

52 Chapter 4

Modelled Antarctic Last Interglacial climate

In this chapter, the Mk3L climate outputs, including those used as inputs for the ice sheet model simulations (Chapter 5), will be analysed.

4.1 Overview

Climate models have been unable to capture the reconstructed magnitude of LIG warming in the high southern latitudes (Lunt et al., 2013; Capron et al., 2014; Hoffman et al., 2017). This may reﬂect several factors, including poor integration of the effects of polar ampliﬁcation in the current generation of climate models, or perhaps uncertainty in the reconstruction of LIG CO2 concentrations (Fischer et al., 2018). In order to explore the impact that greater CO2 concentrations would have had on the LIG Antarctic atmosphere and ocean, a suite of GCM experiments were performed in which CO2e increased incrementally with each model run.

The LIG PMIP3 experiment uses a peak CO2 concentration of 287 ppm (Lunt et al., 2013), which is closest to the Dome C record of 286 ppm, and is 7 ppm lower than the Talos Dome record presented by Schneider et al. (2013). The Mk3L PMIP3 experiment for the LIG is adopted as the initial experiment in this study (σ0) (Lunt et al., 2013). σ represents standard deviation and is the standard error of the measurement uncertainty in the ice core trace gas records. Peak CO2 increases for each experiment by σ2, σ4, σ6, σ8 and σ10 (Table 4.1) to account for the fact that the ice core records may underestimate LIG peak CO2. This encompasses the upper bound of the ice core trace gas records, at σ4 (Köhler et al., 2017) and slightly beyond to account for other processes that may cause the model to underestimate warming. These experiments test the LIG climate response to a full range of possible GHG concentrations. The control climate for this series of experiments is pre-industrial.

53 This chapter will examine Mk3L outputs used as inputs for the ice sheet model simulations (Chapter 5), and will seek to explain these datasets in the broader context of the Mk3L climate simulations and the LIG palaeo-climate records.

4.2 General Circulation Model (GCM) outputs

To examine the impacts of enhanced green house gas concentrations on LIG climate, a series of global climate model simulations were undertaken. Anomalies were calculated by averaging the outputs over 5000 years, from 130 to 125ka, which incorporates peak CO2 at 128.4 ka, and subtracting this from the pre-industrial control. The experiments were undertaken by Dr. Steven Phipps, with all data analysed, gridded and incorporated into PISM by the author.

Average air temperature and ocean temperatures increase as GHG concentrations rise (Table 4.1). Antarctic ice core records suggest LIG surface air temperature warming of 1.5◦ ± 1.5◦C to 2.5◦ ± 1.5◦C relative to present day, however the CCSM3 and HadCM3 climate models simulate a LIG climate equivalent to present day (Capron et al., 2014). In this series of experiments, Mk3L generates mean temperature anomalies from 0.3◦ to 0.9◦C relative to pre-industrial. The highest

CO2 concentration was not sufﬁcient to raise surface air temperatures to the levels found in the palaeo-record.

High southern latitude SSTs have been estimated between 1.1◦ ± 0.6◦C relative to pre-industrial and 3.9 ± 2.8 relative to present day (Hoffman et al., 2017; Capron et al., 2014). The Mk3L simulations underestimate this warming, demonstrating a mean ocean temperature anomaly range of only 0.1◦ to 0.4◦C above pre-industrial. However, proxy data for the Southern Ocean is limited and estimated uncertainties associated with chronologies and temperature-averaging methods in the marine sediment core records are, on average, ∼2.6◦C (Capron et al., 2014).

Table 4.1: Peak CO2e, and mean Antarctic mid-depth ocean and surface air temperature simulated anomalies in relation to the pre-industrial control simulation for each σ forcing experiment.

Mean ocean Mean air Peak CO e Experiment 2 temp anomaly temp anom (ppm) (◦) (◦) σ0 284.1 0.1 0.3 σ2 291.5 0.1 0.5 σ4 298.2 0.2 0.6 σ6 304.9 0.3 0.7 σ8 311.7 0.3 0.8 σ10 318.5 0.4 0.9

54 4.2.1 Atmosphere

Surface air temperature

Surface air temperature increases with greater GHG concentrations (Figure 4.1). For all σ perturbations, the highest temperature anomalies are found close to the Amery sector on the coast of East Antarctica. Under σ6 to σ10 forcing scenarios, the peak surface air temperature anomaly reaches 5◦C above pre-industrial.

σ0 to σ6 forcing generates a negative temperature anomaly of <-1◦C over the Weddell Sea which decreases in size and magnitude as GHG concentrations increase. Under σ8 forcing, this air temperature anomaly becomes positive and remains positive under a σ10 climate regime. A further negative temperature anomaly of <-1◦C remains over the centre of the EAIS for all climate perturbations. This negative anomaly decreases in size with increasing GHG concentrations. Possible reasons for this will be discussed in Section 4.3.

Figure 4.1: Surface air temperature anomalies generated from Mk3L. A:Control, B: σ0, C: σ2, D: σ4, E: σ6, F: σ8, G: σ10.

The simulated surface air temperature anomalies at each ice core location underestimate warming (Figure 4.2). Although the σ8 and σ10 simulations are within the lower bounds of the proxy record at Dome C (Table 4.2), there is a difference of >1◦ between the Dome Fuji simulations and the proxy record.

55 Table 4.2: Simulated surface air temperature anomalies (◦C) at each ice core location

Ice Core Proxy record σ0 σ2 σ4 σ6 σ8 σ10 Vostok 0 - 3.00 -0.47 -0.54 -0.38 -0.26 -0.36 -0.29 Dome C 0.50 - 3.00 0.10 0.32 0.35 0.60 0.54 0.70 Dome Fuji 1.00 - 3.50 -0.21 -0.28 -0.17 -0.11 -0.27 -0.28 EPICA-DML NA 0.14 0.37 0.41 0.67 0.65 0.80 TALDICE NA 0.81 1.17 1.11 1.27 1.34 1.56

Figure 4.2: Location of East Antarctic ice cores which contain LIG climate record, and simulated and proxy record (PR) surface air temperature anomalies (◦C) at each site for different scenarios.

56 Precipitation

There are large increases in precipitation around the coast of much of the EAIS (Figure 4.3). Under σ4 forcing, a precipitation anomaly of >40 mm a−1 is simulated over the Aurora Basin. The area expands along the coast with each increase in GHG concentration. This area stretches from the Amery to the Dry Valleys under σ6 to σ10 climate forcings. Much of the Antarctic interior exhibits no, or very small precipitation anomalies since there is very little precipitation in this region.

The areas of increased precipitation correspond with areas of reduced sea ice cover (Figure 4.6). A reduction in sea ice leads to warmer air as a result of reduced albedo effect, this warmer air drives the increase in precipitation. This will be further discussed in Section 4.3.

Figure 4.3: Precipitation anomalies generated by Mk3L. A: Control, B:σ0, C: σ2, D: σ4, E: σ6, F: σ8, G: σ10.

57 4.2.2 Ocean

Ocean temperatures

Ocean temperature anomalies increase as CO2 levels increase (Figure 4.4), with the greatest warming occurring in the top 2000 m of the water proﬁle (Figure 4.5a). The highest temperature anomalies are simulated in the Amery sector, which experiences 1◦ under σ10 forcing. Under σ0 forcing, there is very little increase in ocean temperature.

Figure 4.4: Mid-depth (400-600 m) ocean temperature anomalies. A: Control, B: σ0, C: σ2, D: σ4, E: σ6, F: σ8, G: σ10.

58 (a) A: σ0, B: σ2, C: σ4, D: σ6, E: σ8, F: σ10.

(b) A: σ0, B: σ2, C: σ4, D: σ6, E: σ8, F: σ10.

Figure 4.5: Southern hemisphere (pole to equator) ocean temperature anomaly proﬁles. a) Total average; b) Weddell Sea sector (0◦ to 60◦E); c) Ross Sea sector (170◦E to 150◦W).

59 4.2.3 Sea ice

Sea ice is an important component of the cryosphere. Sea ice loss can lead to warmer temperatures due to loss of albedo, and this can lead to increased precipitation. Sea ice also acts as a barrier which protects ice shelves from ocean swell (Massom et al., 2018), and modelling has shown that it can prevent warm bottom water from intruding on the continental shelf (Naughten et al., 2018).

For all experiments, there is a positive sea ice anomaly around the WAIS. The largest area of sea ice increase is in the Weddell Sea where there is a 10-15% increase under σ0 conditions. This positive anomaly decreases slightly as CO2 concentrations increase (Figure 4.6). There is a negative sea ice cover anomaly in the East Antarctic for all the experiments, and this negative anomaly becomes greater as CO2 levels increase. Under σ8 and σ10 conditions, there is a 50% decrease in sea ice cover in the Indian Ocean sector, around the Davis Sea. High ocean temperature anomalies are concentrated in this area, which accounts for the decrease in sea ice cover.

Figure 4.6: Sea ice anomalies. A: Control, B: σ0, C: σ2, D: σ4, E: σ6, F: σ8, G: σ10.

60 4.2.4 Ocean salinity

As Mk3L is not coupled to an ice sheet model, salinity anomalies are small and strongly linked to the Antarctic Circumpolar Current (ACC) (Figure 4.6). Positive salinity anomalies decrease with increasing CO2 concentrations. There is an increased freshening of the Weddell Sea, which leads to a slow-down of AABW formation (see Section 4.2.5).

Figure 4.7: Mid-depth ocean salinity anomalies from Mk3L. A: Control, B: σ0, C: σ2, D: σ4, E: σ6, F: σ8, G: σ10.

61 4.2.5 Antarctic Bottom Water formation

Simulated Antarctic Bottom Water (AABW) formation is concentrated in the Weddell Sea (Figure 4.8). Under σ0 forcing, there is a positive convective depth anomaly. This is largely maintained under σ2 conditions, but a negative anomaly emerges under σ4 forcing. This negative anomaly becomes greater as CO2 increases, and under σ10 perturbation, convective depth is reduced by 800 m suggesting a slowdown in the AMOC.

Figure 4.8: Convective depth anomalies. A: Control, B: σ0, C: σ2, D: σ4, E: σ6, F: σ8, G: σ10.

Analysis of meridional ocean circulation shows that increased GHG concentrations lead to a slowdown of the North Atlantic Deep Water (NADW) and a decrease in the production of AABW (Figure 4.9b). AABW formation in the model simulations is markedly reduced with increasing CO2. This reduction is focused across the Weddell Sea, which is a key region for AABW production, accounting for some 60% of contemporary AABW production today. AABW production in the Weddell Sea forms the lower limb of AMOC, and these simulations suggest that enhanced CO2 affects AABW formation in this region disproportionally (Figure 4.10).

For all simulations both the NADW and AABW anomalies are less than 10% of the control value. Under σ0 climate perturbations, the reduction in flow does not extend much further south than 70◦S. As the σ value increases, the area of flow reduction expands and intensifies. Under σ4 climate forcing, flow in the Weddell Sea is affected, and this is also reflected in the convection depth data (Figure 4.8). This suggests that σ4 is the threshold above which AABW production reduces (see Section 4.3 for further discussion).

62 (a) Control meridional overturning. AAIW: Antarctic Intermediate Water; NADW: North Atlantic Deep Water; AABW: Antarctic Bottom Water.

(b) Meridional overturning anomalies. A: σ0, B: σ2, C: σ4, D: σ6, E: σ8, F: σ10.

Figure 4.9: Average meridional overturning across the Southern Hemisphere. a) Control, b) Anomalies.

63 (a) Control Atlantic meridional overturning. AAIW: Antarctic Intermediate Water; NADW: North Atlantic Deep Water; AABW: Antarctic Bottom Water.

(b) Atlantic meridional overturning anomalies. A: σ0, B: σ2, C: σ4, D: σ6, E: σ8, F: σ10.

Figure 4.10: Average Atlantic meridional overturning for the Southern Hemisphere. a) Control, b) Anomalies. Note that AAIW is negative for the control, and so positive anomalies represent a slow-down.

64 4.3 Discussion

Climate models have been unable to capture the magnitude of LIG warming in the southern high latitudes. Here, the Mk3L outputs will be compared and contrasted with the palaeo-records.

4.3.1 Sea ice cover, air temperatures & precipitation

The LIG has been associated with major retreat of Antarctic sea ice. Analysis of sea salt fluxes in ice cores has suggested a >50% reduction in Antarctic winter sea ice at the onset of the LIG (Holloway et al., 2017). Holloway et al. (2017) found that the records indicate little to no sea ice retreat in the Pacific sector of the Southern Ocean, and major sea ice retreat in the Atlantic and Indian sectors of the Southern Ocean. Although the marine core sites are too far north to provide independent verification of the ice core data, these results partially validate the model simulations in this study. The simulated sea ice cover from Mk3L demonstrates a significant reduction in sea ice cover in the Indian Ocean sector under σ forcings of σ4 and above, but little to no loss of sea cover under any of the climate forcings in the Atlantic sector of the Southern Ocean.

The results of this study correspond with Huybers and Denton(2008), who proposed that Antarctic sea ice loss is driven by higher surface air temperatures from increased insolation. This work has been explored further in recent modelling studies that found decreased sea ice cover leads to increased surface air temperatures as a result of reduced albedo, creating a positive feedback (Langebroek and Nisancioglu, 2014). The increase in surface air temperature also causes precipitation rates to rise.

The warming over the Amery sector corresponds well with the local sites of early warming in East Antarctica of 5◦C above present day (AD 1961-1990) (Turney and Jones, 2010; Jouzel et al., 2007), providing conﬁdence in the result.

Some mechanisms which can have an impact on precipitation rates are not included in the Mk3L simulations, as there is no coupled ice sheet. A lower Antarctic ice surface has been associated with intensiﬁed cyclones over the ice sheets (Walsh et al., 2000). This intensiﬁcation allows more storms to travel over the continent, bringing extra moisture inland (Steig et al., 2015). The loss of the WAIS can also change precipitation rates and patterns, however this cannot be accounted for in these experiments due to the absence of a coupled ice sheet model.

Although Mk3L can simulate the features of the zonal-mean circulation well, the core of the Southern Hemisphere westerlies are slightly too far north compared to observations (Phipps et al., 2011). This could be a factor that causes to the underestimate of warming across Antarctica.

65 The following section will explore LIG ocean changes.

4.3.2 Ocean

The polar seesaw mechanism suggests that warming should be most intense in the Atlantic sector of the Southern Ocean (Weddell Sea) as a result of weakening of the Atlantic Meridional Overturning Circulation (AMOC) caused by early northern hemisphere warming and associated meltwater input from early northern hemisphere warming (Stocker and Johnsen, 2003; Marino et al., 2015). Although there is some warming in the Weddell Sea as the σ perturbation increases, it is smaller than the average warming for the Southern Ocean (Figure 4.5). Duplessy et al. (2007) analysed oxygen isotopes from benthic foraminifera in Atlantic and Southern Ocean deep sea cores and found that NADW during the LIG was 0.4◦ ±0.2◦C warmer than present. This compares favourably with the modelled results under σ0 to σ8 climate forcing, however, the temperature proﬁle under σ10 perturbation exhibits a warmer NADW anomaly of >1◦C.

Circumpolar Deep Water (CDW) is a constituent part of the Antarctic Circumpolar Current (ACC). It can be up to 4◦ above freezing, and as it mixes with cooler surface waters on the continental shelf, it can still be warmer than 1◦ (Wåhlin et al., 2010). This process is a key driver of ice shelf and ice stream thinning (Pritchard et al., 2012; Payne et al., 2004; Rignot et al., 2002). With greater GHG concentrations, these simulations exhibit warmer mid-depths ocean temperatures concentrated on the coast of East Antarctica; in the Riiser-Larsen, Cosmonauts and Cooperation Seas, and around the Amery Ice Shelf (Figure 4.4). This warmer water is advected clockwise via the ACC, however, the AP and the formation of AABW act as a barrier, preventing the delivery of warm water via the ACC to the Weddell Sea. Present day observations show that strengthening westerlies cause the intrusion of CDW onto continental shelf of the Amundsen Basin and the Western AP (Wåhlin et al., 2010; Moffat et al., 2009). This pattern is replicated in these simulations, and becomes stronger with each σ perturbation. This indicates warmer global temperatures that lead to a southerly shift and intensiﬁcation of the mid-latitude westerly winds.

Meridional overturning

The AMOC before the Last Glacial Maximum is poorly constrained due to sparse proxy records, especially in the high southern latitudes (Luo et al., 2018). Samples from core sites in the northern North Atlantic indicate a signiﬁcant weakening of the AMOC at ∼125 ka, followed by a gradual increase in strength until ∼115 ka (Galaasen et al., 2014). Results from a core in the NW Atlantic produced similar results (Deaney et al., 2017), however an interpretation by Bohm et al. (2015) found that the AMOC was strong and deep throughout the LIG. A recent modelling study by Luo et al. (2018) indicated only a slight slowdown in the AMOC; at 115 ka, the simulated AMOC was found to be ∼0.5 Sv weaker and 100 m shallower than PI, and by 125 ka, the simulated AMOC had strengthened by 1.5 Sv and was 200 m deeper than PI (Luo et al.,

66 2018).

Observations show that the Weddell Gyre is the primary region of AABW formation, with >60–70% of all AABW production occurring here (Orsi et al., 2002; Jullion et al., 2014). Mk3L captures this spatial focus of ABBW well, however, it does exaggerate the rate of AABW production, and underestimates the rate of NADW formation. This may be due to the slight northerly bias in the positioning of the southern westerlies which have an effect on upwelling (Anderson et al., 2009; Denton et al., 2010). Hayes et al. (2014) examined marine sediment core samples from the Southern Ocean and found evidence of decreased oxygenation of deep water by AABW during the LIG. This suggests that the σ4 to σ10 climate perturbations, which exhibit a decrease in convective depth (Figure 4.8), are more realistic than those experiments forced with a lower CO2 concentration. σ0 perturbation does not result in a decrease in convective depth. Jullion et al. (2014) used hydrographic data and an box inverse model of the Weddell Gyre to quantify this area’s present day contribution to Global Overturning Circulation. They found that AABW is produced at a rate of only 6 ± 2 Sv, which is <5% of the rate simulated by Mk3L. This inability to realistically capture high-latitude salinity and AABW production is believed to be the cause of the cold bias in the deep ocean that this model, and other GCMs, generate (Phipps et al., 2011).

4.4 Conclusion

This series of climate experiments demonstrate that LIG Antarctic warming could have been driven primarily by the delivery of warm ocean water from lower latitudes due to a southerly shift and intensiﬁcation of the westerly winds, and corresponding changes to CDW. This warm water caused a reduction of sea ice cover, leading to increased surface air temperatures and precipitation. These results support the hypothesis that AMOC is key to the evolution of global LIG temperatures (Bakker et al., 2013), and suggest that Antarctic change is driven by changes to global circulation, rather than local astronomical forcing.

Climate models have so far been unable to capture the magnitude of LIG warming in the southern high latitudes, possibly due to a slight northerly bias in the positioning of the westerlies. These experiments show that increased GHG concentrations generate LIG simulations which share an improved correlation with the proxy records. However, warming is still underestimated and better knowledge of the temporal and spatial distribution, and magnitude of melt-water ﬂuxes is required to capture meridional overturning. Ideally, (without the constraints of computational expense) a fully-coupled climate-ocean-ice-sheet model should be used.

Chapter 5

Antarctic contribution to sea level rise during the Last Interglacial

5.1 Overview

The interpolated Mk3L climate anomalies were applied to present day surface air temperature and precipitation data from RACMO2.3 (Figures 5.1b and 5.2b). Mid-depths ocean temperature and salinity anomalies were applied to a uniform grid of predetermined values (Figures 5.3a and 5.3b). This data was used to drive PISM in a suite of experiments to investigate the Antarctic ice sheet response to a range of GHG concentrations under a LIG orbital conﬁguration. Upper and lower melt estimates were acquired through running experiments with and without the basal melt interpolation function respectively.

Although previous studies have applied a transient LIG climate to present day or Last Glacial Maximum (LGM) ice sheet geometry (DeConto and Pollard, 2016; Sutter et al., 2016), this approach is not valid since transient climate experiments should be run through a full glacial cycle and climate inputs that cover the previous glacial cycle are not available. Constant climate forcing was used in these experiments because, although this approach may not be considered realistic, it does give a ﬁrst-order indication of potential long-term mass loss under a given climate.

The full range of simulations undertaken here suggest ice loss ranges from 16 to 648 cm SLE (sea level equivalent) over 20 kyrs of constant climate forcing, with the majority of mass loss occurring within the ﬁrst 1500 years. The basal melt interpolation increases mass loss by 143 to 353 cm SLE. Atmospheric forcing alone leads to increased precipitation and subsequent ice sheet growth, and so this rapid mass loss appears to be primarily driven by ocean-warming. Ice loss is constrained by topography, with marine ice-sheet instability having signiﬁcant impacts across key sectors of Antarctica.

69 5.2 Climate perturbations

PISM accepts four climate boundary conditions: surface air temperature (Figure 5.1), precipitation (Figure 5.2), sea surface temperatures (Figure 5.3a), and ocean salinity (Figure 5.3b). The Mk3L anomalies, which were calculated by averaging the outputs over 5000 years, from 130 to 125ka, were re-gridded and interpolated using tension spline interpolation.

Both the Mk3L surface air temperature and precipitation anomalies, and the PISM input ﬁles are plotted below (Figures 5.1b and 5.2b). The Mk3L anomalies have already been discussed in Chapter 4, and so only the PISM input datasets will be outlined here.

5.2.1 Surface air temperature

The warmest surface air temperatures are found over the AP, especially the Bellingshausen Sea area, and above the Pine Island Glacier (PIG) catchment (Figure 5.1b). Surface air temperature remains below freezing across Antarctica under all σ perturbations. There is a cooling temperature anomaly over the Weddell Sea under σ0 to σ6 perturbations. The is due to the AP deﬂecting the warmer air found on the western edge northward.

5.2.2 Precipitation

The highest precipitation levels are also found over the AP, on the West coast of the WAIS, and the coastline of the Aurora Basin (Figure 5.2b). Precipitation rates across the ice sheet remain close to 0. The high control precipitation over the Aurora Basin coast is further increased by a large, positive precipitation anomaly.

5.2.3 Sea surface temperatures & salinity

Mid-depth ocean data averaged over 400 to 700 m, were passed to PISM as the model can currently only accept one ocean field and does not have the capability to simulate the effect on the ice of changing temperatures across a full depth-profile. Tension spline interpolation was used to create the under-ice temperature field. Although this method can introduce uncertainties due to a sensitivity to outliers, it performs better than other methods (see Appendix A). The anomalies were applied to uniform-value grid, and so the patterns of change are identical to those discussed in Chapter 4.

70 (a) Surface air temperature anomalies. A: σ0, B: σ2, C: σ4, D: σ6, E: σ8, F: σ10.

(b) Surface air temperature. A: σ0, B: σ2, C: σ4, D: σ6, E: σ8, F: σ10.

Figure 5.1: a) Surface air temperature anomalies generated by Mk3L. b) Surface air temperature anomalies applied to the RACMO2.3 dataset (Figure 3.5a). This data was used to drive PISM.

71 (a) Precipitation anomalies A: σ0, B: σ2, C: σ4, D: σ6, E: σ8, F: σ10.

(b) Precipitation A: σ0, B: σ2, C: σ4, D: σ6, E: σ8, F: σ10.

Figure 5.2: a) Precipitation anomalies generated by Mk3L. b) Precipitation anomalies applied to the RACMO2.3 dataset (Figure 3.5b). This data was used to drive PISM.

72 (a) Mid-depths ocean temperature. A: σ0, B: σ2, C: σ4, D: σ6, E: σ8, F: σ10.

(b) Ocean salinity. A: σ0, B: σ2, C: σ4, D: σ6, E: σ8, F: σ10.

Figure 5.3: Ocean inputs a) Mid-depths ocean temperature anomalies applied to a uniform grid set to freezing point, b) Mid-depths salinity anomalies set to a uniform grid of a pre-determined value.

73 5.3 Contribution to sea level

The application of the Mk3L climate outputs to PISM exhibit a wide range of mass loss. Simulated ice losses range from 16 to 648 cm SLE (sea level equivalent) (Figure 5.4). For the lower-estimate experiments, the σ0 scenario perhaps unsurprisingly generates the lowest mass loss (16 cm SLE), followed by σ2 (191 cm SLE), σ4 (197 cm SLE), and σ6 (258 cm SLE).

Here, the climate scenarios with the highest CO2 concentrations, σ8 and σ10, produce the greatest mass loss, with both contributing 454 cm SLE. For the more extreme, upper-estimate experiments, σ0 simulates the lowest contribution to sea level (168 cm SLE), followed by σ2 (461 cm SLE), σ4 (498 cm SLE), σ8 (597 cm SLE), σ6 (611 cm SLE), and σ10 (648 cm SLE). Intriguingly, mass loss in the upper-estimate σ8 experiment is less than the σ6 experiment.

The upper and lower bounds of the melt estimates are closest for σ8, with a difference of 143 cm SLE, and greatest for σ6 which has a difference of 353 cm SLE. There is a 152 cm SLE difference between the σ0 experiments, a 194 cm SLE range for the σ10 experiment, and a 270 cm SLE range for σ2.

Figure 5.4: Sea level equivalent contribution from Antarctica for all σ climate scenarios. Solid lines represent lower-estimate experiments, without basal melt interpolation, dashed lines represent higher- estimate experiments, with basal melt interpolation.

74 5.4 Areas of mass loss

Mass loss is not distributed evenly across ice sheets, and the following section will explore which areas of Antarctica are most vulnerable to the Mk3l climate perturbations.

The lower-estimate experiments which do not implement the basal melt interpolation function, are discussed ﬁrst, followed by the upper-estimate experiments.

5.4.1 Lower estimates

Under σ2 and higher GHG scenarios, the Ross Sea sectors show the highest sensitivity to climate forcing (Figure 5.5). As the CO2 concentration increases and the WAIS loses mass, the grounding lines of the Weddell and Amundsen Sea sectors do not move. Topographical constraints, as well as a negative ocean temperature anomaly in the Weddell Sea (Figure 4.4) allow a portion of the Ronne-Filchner ice shelf to remain intact in the σ10 scenario, despite losing most of its ice source.

Figure 5.5: Ice surface elevation at 20 kyrs for each climate scenario. A: σ0, B: σ2, C: σ4, D: σ6, E: σ8, F: σ10. Note the apparent vulnerability of the Ross Ice Shelf.

75 To further investigate how mass is lost across the continent, the ice sheets are divided into sectors according to the GSFC drainage systems dataset (Zwally et al., 2012). This dataset was developed by the Ice Altimetry Group at Goddard Space Flight Center using ICESat data to identify drainage systems according to ice provenance and it enables the analysis of mass loss by catchment area. Due to overall mass gains in some experiments, some sectors can lose over 100% of the total mass lost.

Sector analysis: σ0

Mass loss is more evenly spread across the sectors for the σ0 simulation than for subsequent experiments with higher GHG concentrations (Figure 5.6). Under the σ0 scenario, the greatest mass loss is from the western edge of the Antarctic Peninsula (AP) (56 cm SLE). This catchment loses more mass in this scenario than in any other experiment. Interestingly, under the σ0 scenario, the ice sheet shows signiﬁcant gains (73 cm SLE) in the Pine Island and Thwaites catchments.

76 Figure 5.6: Mass loss by catchment area under constant σ0 forcing over 20kyrs. Shading shows % of total mass loss. Lost ice volume in sea level equivalent (cm) is labelled for each catchment.

Sector analysis: σ2

Although peak CO2 is only 4 ppm higher in the σ2 climate scenario than the σ0 scenario, the ice sheet response is markedly different (Figure 5.7). The simulated EAIS does not lose as much mass in the σ2 climate scenario, but the most prominent difference is the switch in the sectors that make the greatest contribution to ice mas loss. Under the σ2 scenario, the Ross Sea sectors of the WAIS have become the largest contributors to SLR (145 cm SLE). In contrast, the Pine Island and Thwaites sectors both lose 6 and 10 cm SLE mass respectively, and the western AP mass loss is only 1 cm SLE.

77 Figure 5.7: Mass loss by catchment area under constant σ2 forcing over 20kyrs. Shading shows % of total mass loss. Lost ice volume in sea level equivalent (cm) is labelled for each catchment.

78 Sector analysis: σ4

Under further increases in GHGs, the EAIS appears to contribute less mass loss. Simulated mass loss from the EAIS is not as great for the σ4 climate scenario as it is for the σ0 and σ2 experiments (Figure 5.8). As with the σ2 scenario, the Ross sectors of the WAIS are the greatest contributors to SLR. The amount of mass loss from these two sectors is 10 cm SLE higher (155 cm SLE) for this climate scenario than the σ2 scenario. The Pine Island, Thwaites and western Antarctic Peninsula remain relatively low contributors.

Figure 5.8: Mass loss by catchment area under constant σ4 forcing over 20kyrs. Shading shows % of total mass loss. Lost ice volume in sea level equivalent (cm) is labelled for each catchment.

79 Sector analysis: σ6

Most mass loss continues to be focused on the Ross Sea sectors of the WAIS in the σ6 experiment, with 172 cm SLE from this area (Figure 5.9). Sea level contribution from the Pine Island and Thwaites catchments of the Amundsen sector increases slightly, from 18 cm SLE in the σ4 scenario, to 29 cm SLE. The western AP had been in equilibrium for the σ4 scenario, but the σ6 simulation generates 13 cm SLE from this catchment. Mass loss from the Amery sectors also shows a very slight increase.

Figure 5.9: Mass loss by catchment area under constant σ6 forcing over 20kyrs. Shading shows % of total mass loss. Lost ice volume in sea level equivalent (cm) is labelled for each catchment.

80 Sector analysis: σ8 and σ10

Both the σ8 and σ10 experiments lose the same total amount of mass. Under these scenarios, the Amundsen and Ross sectors of the WAIS collapse, leading to substantial loss equivalent to 379 cm SLE (84% of total mass loss) from these four catchments under σ8 forcing (Figure 5.10), and 363 cm SLE (79% of total mass loss) under σ10 forcing (Figure 5.11).

The Ross Sea sectors of the WAIS continue to be the most sensitive catchments, contributing over 40% of the total mass loss under σ10 climate forcing, followed by the Amundsen sector catchments (39% total mass loss). In contrast, the Weddell Sea sector of the WAIS, contributes just over 8% of the total mass loss under σ10 climate forcing.

The interior of the EAIS thins under σ8 and σ10 forcing, with 10 out of 16 catchments losing mass. The Amery catchments lose 8% of the total mass loss, however, the EAIS remains largely intact. There are some small mass gains in the lower-latitude coastal sectors of the EAIS, such as the Wilkes and Aurora basins, which gain 10% of mass lost under σ10 forcing, despite their reverse-slope beds making them vulnerable to MISI.

Figure 5.10: Mass loss by catchment area under constant σ8 forcing over 20kyrs. Shading shows % of total mass loss. Lost ice volume in sea level equivalent (cm) is labelled for each catchment.

81 Figure 5.11: Mass loss by catchment area under σ10 climate forcing. Shading shows % of total mass loss. Lost ice volume in sea level equivalent (cm) is labelled for each catchment.

82 5.4.2 Higher estimates

The implementation of basal melt interpolation which allows for enhanced basal melt at the grounding line due to the pressure effects of deeper bathymetry, generates an extreme ice sheet response that shows marked differences (and similarities) to the simulated ice sheet without the basal melt interpolation (Section 5.4.1). The most dramatic difference is the collapse of the simulated WAIS at σ2 (Figure 5.12), which leaves a large ice shelf in the area previously occupied by the WAIS that survives until σ8. Although this ice sheet is more sensitive to climate changes, the Wilkes and Aurora basins remain intact even under the most extreme climate scenarios.

Figure 5.12: Ice surface elevation at 20 kyrs for each climate scenario. A: σ0, B: σ2, C: σ4, D: σ6, E: σ8, F: σ10.

83 To investigate the spatial response to different GHG scenarios, the various sectors are explored using the same technique described above (Section 5.4.1).

Sector analysis: σ0

The catchment with the greatest simulated mass loss is the Weddell Sea sector of the WAIS, which loses 42 cm SLE (Figure 5.13). The Pine Island and Thwaites catchments of the Amundsen Sea sector do not show the gains present in the lower-estimate experiment (Figure 5.6). Here the mass loss is focused across the WAIS, particularly the Ross and Weddell Sea sectors.

Figure 5.13: Mass loss by catchment area under constant σ0 forcing over 20 kyrs. Shading shows % of total mass loss. Lost ice volume in sea level equivalent (cm) is labelled for each catchment.

84 Sector analysis: σ2

With the basal melt interpolation function, σ2 appears to represent a major tipping point in the WAIS; here, the WAIS collapses. However, under this scenario, there is only minimal contribution to sea level from the EAIS, with a total mass loss of 33 cm SLE, in contrast to the 427 cm SLE loss from the WAIS (Figure 5.14).

Figure 5.14: Mass loss by catchment area under constant σ2 forcing over 20 kyrs. Shading shows % of total mass loss. Lost ice volume in sea level equivalent (cm) is labelled for each catchment.

85 Sector analysis: σ4

As GHG concentrations are increased under the σ4, the magnitude of mass loss across the WAIS increases slightly compared with σ2 forcing. Most notably, mass loss for the Pine Island sector rises from 77cm SLE under σ2 conditions, to 92cm SLE (Figure 5.15).

Figure 5.15: Mass loss by catchment area under constant σ4 forcing over 20 kyrs. Shading shows % of total mass loss. Lost ice volume in sea level equivalent (cm) is labelled for each catchment.

86 Sector analysis: σ6 and σ8

An interesting aspect of the upper-estimate experiments is that σ6 loses greater mass than σ8. The difference is caused by small variations in the EAIS catchments (Figures 5.16 and 5.17). The small gains found across the Wilkes and Aurora basins are larger under σ8 forcing than all the other experiments (11cm SLE).

Figure 5.16: Mass loss by catchment area under constant σ6 forcing over 20 kyrs. Shading shows % of total mass loss. Lost ice volume in sea level equivalent (cm) is labelled for each catchment.

87 Figure 5.17: Mass loss by catchment area under constant σ8 forcing over 20 kyrs. Shading shows % of total mass loss. Lost ice volume in sea level equivalent (cm) is labelled for each catchment.

88 Sector analysis: σ10

Under the upper-estimate σ10 simulation, the EAIS contributes a total of 80 cm to SLR, compared with 30 cm under the lower-estimate experiment. Correspondingly, the WAIS loses 566 cm SLE under the upper-estimate σ10 simulation, and 423 cm SLE under the lower-estimate experiment.

Figure 5.18: Mass loss by catchment area under constant σ10 forcing over 20 kyrs. Shading shows % of total mass loss. Lost ice volume in sea level equivalent (cm) is labelled for each catchment.

89 5.5 Ice dynamics

Ice velocity at the grounding line plays an important role in marine ice sheet instability (Schoof, 2007). Analysis of simulated basal velocity can give valuable insights into the tipping points and mechanisms of ice sheet collapse.

Both the lower-estimate (Figure 5.19a) and upper-estimate (Figure 5.19b) simulations demonstrate increased velocities at the calving front. Interestingly, there are areas of decreased ﬂow immediately upstream of these areas of increased velocity. In the Ross Sea, these areas of slowdown are associated with pining on new islands that are formed in response to glacio-isostatic adjustment over the 20 kyrs.

The Pine Island and Thwaites glaciers, the Bellingshausen Sea sector on the west of the AP, the Ross sea sectors of the WAIS, and the Amery show the greatest increase in velocity for both the lower-estimate and upper-estimate ice sheet simulations. The Davis Sea sectors near to the Amery see large increases in surface velocity in the upper-estimate simulation. Velocity changes are greater in the upper-estimate simulation, with some ice streams demonstrating a >1000 m a−1 increase in velocity at the grounding-line in the σ0 scenario.

90 (a) A: σ0, B: σ2, C: σ4, D: σ6, E: σ8, F: σ10.

(b) A: σ0, B: σ2, C: σ4, D: σ6, E: σ8, F: σ10.

Figure 5.19: Surface velocity anomalies for each climate scenario: a) Lower-estimate experiments, b) Higher-estimate experiments.

91 5.6 Drivers

Overall, mass loss increases with rising GHG concentrations, however, it is important to consider whether the key driver of mass loss is the atmosphere or ocean. The conservative ice sheet simulations were repeated ﬁrstly with atmospheric forcings applied, but ocean inputs kept at freezing point, and secondly with ocean forcings applied, but atmospheric inputs held at present day values, which were used to tune the model, and therefore hold the modelled ice sheet at steady state.

As Figure 5.20 shows, ice sheet volume increases with GHG concentration where climate offsets are applied to the atmosphere alone. Where climate offsets are applied to the ocean alone, ice loss is much closer to the results seen where ocean and atmosphere offsets are applied.

Figure 5.20: Ice volume anomaly in sea level equivalent for all σ climate scenarios. Solid lines represent atmosphere and ocean forcings combined. Dotted lines represent forcings applied only to the atmosphere, dashed lines represent forcings applied only to the ocean.

Simulations where LIG climate perturbations are only applied to the ocean see increased mass loss, despite the reduction in surface melt (Figure 5.21). This is due to the loss of the increased precipitation over the Wilkes and Aurora basins (Figure 5.2a). The differences between ocean and atmosphere, and ocean only forcing are smallest for the σ6 and σ8 experiments as warmer temperatures lead to increased precipitation, which offsets the increased losses through ocean melt. Increased precipitation is unable to offset the increases in ocean-driven mass loss seen in experiment σ10 , resulting in the largest ice volume difference between ocean and atmosphere forcing, and ocean only forcing. These experiments highlight the importance of ocean warming on ice sheet dynamics and ice sheet collapse, as mass loss through increased surface melt is

92 largely offset by increased precipitation in a warmer world.

Figure 5.21: Ice thickness differences between the ocean forced experiments and the combined atmosphere and ocean forced experiments for all σ climate scenarios. A: σ0, B: σ2, C: σ4, D: σ6, E: σ8, F: σ10.

93 5.7 High resolution experiments

To ensure the results are robust, the lower-estimate experiments were repeated at a 10 km resolution in order to assess whether, or to what extent, the model outputs are resolution-dependent (Figure 5.22). Resolution has the smallest impact on the σ10 experiment, and the largest impact on the σ2 experiment. The higher resolution experiments conserve greater mass than the lower resolution experiments, however, differences are <70 cm SLE for all the experiments.

The differences in mass loss due to resolution changes are associated with the modelling of outlet glaciers and small ice streams. They are non-linear because the location of topographic thresholds and pinning points varies with resolution.

Figure 5.22: Ice volume in sea level equivalent anomaly for all σ climate scenarios at 20 km resolution (solid line) and 10 km resolution (dashed line).

5.8 Discussion

The implementation of both a high and low estimate experiment setup, combined with a range of different climate scenarios helps to limit the effect of uncertainties in the input data, generating a robust result. This series of experiments demonstrate that, on a millennial scale, the potential for large changes in ice loss with small increases in GHG concentration under LIG orbital forcing is signiﬁcant. It is, therefore, important to consider the uncertainties in the palaeo-record of past

CO2 concentration when reconstructing past environments. The present upper-bound of uncertainty in the ice core record is σ4 (Köhler et al., 2017) and, although the peak in LIG CO2

94 was much shorter than the 20 kyr forcing applied here, these results show that it is sufﬁcient to generate increased mass loss from the Antarctic ice sheets.

Mass loss is driven by ocean melt, with topography dictating which areas are most susceptible to warming sea temperatures. Even under climate scenarios where much of the WAIS is lost, the Ellsworth Mountains, Marie Byrd Land and the Antarctic Peninsula remain intact. These results emphasise the importance of marine ice sheet instability (MISI) in West Antarctica, as well as provide support to the theory of Bamber et al. (2007) who identiﬁed these areas as regions to be glacially stable, and are compatible with the ﬁndings of Hein et al. (2016) and Fogwill et al. (2016) (see Chapter 2).

Although increased precipitation (Figure 5.2a) due to warmer temperatures (Figure 5.1b) and reduced sea ice coverage (Figure 4.6) leads to positive mass balance in the Wilkes and Aurora basin sectors of the EAIS, the impact is very small. The high-estimate σ8 experiment is the only simulation where increased precipitation shows a significant effect and ice loss is less than the loss under σ6 forcing (Figure 5.4). However, the difference between σ6 and σ8 is only 14 cm SLE and the small gains in mass are not large enough to be reflected in the basal velocity or the grounding-line position (Figure 5.12). Overall, the Wilkes and Aurora basins are shown to be robust, as they remain intact after 20 kyrs continuous forcing under peak interglacial conditions even where precipitation anomalies are removed from the climate forcing (Figure 5.21). The Amery and Weddell Sea sectors are found to be the most vulnerable sectors of the EAIS. Although the contribution of these sectors is significant (up to 50 cm SLE), it is small when compared with the collapse of the WAIS.

The cool, dense bottom water that forms in the Weddell Sea prevents the intrusion of CDW onto its continental shelf (Hellmer et al., 2017; Darelius and Sallee, 2018), and so a portion of the Ronne-Filchner ice shelf survives for all the climate scenarios. Although warmer water does reach the Weddell Sea grounding line from the Amundsen Sea, this is due to the interpolation methods used.

Despite a difference of only 7 ppm CO2e, there is a clear tipping point between the σ0 and σ2 climate scenarios under both the upper and lower-estimate experiments. Under the lower-estimate experiment, mass loss becomes focused on the Ross Sea sector of the WAIS in σ2, and the gains seen in the σ0 Amundsen Sea sector do not occur. Under the higher-estimate experiments, σ2 is the point where the WAIS collapses and 461 cm SLE is lost. Under the lower-estimate scenarios, this does not take place until σ8.

σ4 represents the CO2 level closest to the upper bound of the ice core record (Köhler et al., 2017). This experiment generates mass loss of 197 to 498 cm SLE, which is similar to the ﬁndings of Sutter et al.(2016).

95 Isostatic rebound has a small, but clear effect on ice sheet dynamics (Figures 5.19). Although it cannot prevent collapse of the WAIS, it does appear to decrease ice velocity behind the grounding-line along the Siple coast (Figure 5.19) and this is likely to moderate the rate of mass loss.

5.8.1 Low- vs. high-estimate experiments

The loss of the Ross ice shelf triggers WAIS collapse under the lower-estimate scenarios. Under the high-estimate experiments, the WAIS is so severely undercut that the ice sheet becomes an ice shelf under the σ2, σ4 and σ6 climate forcings. This ice shelf is unable to form without the Ross Ice Shelf pinning point and this raises the interesting possibility that the WAIS could collapse without losing its major ice shelves. Although the basal melt interpolation function has a sound basis in physics, it is thought to exaggerate the ice sheet melt response (see Section 3.4.3) and it’s extremely unlikely that an ice shelf of this size could be sustained without substantial feeder-glaciers. However, the ice-free channel simulated under the lower-estimate scenarios is also unrealistic as it’s probable that ice mélange and icebergs would have a buttressing effect on the remaining ice sheet in this area. Fully-coupled experiments would need to be carried out to assess the sea ice response to WAIS collapse and what inﬂuence this has on the surviving ice sheet.

The Pine Island Glacier (PIG) sector of the WAIS exhibits a high sensitivity to the basal melt interpolation function. Under lower-estimate conditions, σ0 forcing drives mass gain in both the PIG and Thwaites sectors of 37 and 36 cm SLE, respectively (Figure 5.6). However, under higher-estimate conditions, the PIG loses 20 cm SLE when forced by the σ0 climate, and the Thwaites mass-gain is reduced to 1 cm SLE (Figure 5.13). Current observations have demonstrated that the PIG is highly sensitive to ocean warming and is vulnerable to MISI (Shepherd et al., 2004; Payne et al., 2004; Rignot et al., 2014), and these ﬁndings appears to reﬂect this. Under σ0 forcing, ocean temperatures in the Amundsen Sea are slightly above freezing, there is a small positive precipitation anomaly in this area (Figure 5.2a), ice thins in this sector when atmospheric anomalies are removed (Figure 5.21), and surface velocity is higher in this catchment under the lower-estimate conditions (Figure 5.19a).

Interestingly, where the PIG gains mass under lower-estimate σ0 conditions, the Western AP loses 56 cm SLE ice volume, and where the PIG loses mass under higher-estimate σ0 conditions, the Western AP gains 3 cm SLE ice volume. This area gains mass when atmospheric anomalies are removed from the climate forcing (Figure 5.21), suggesting this sector is more sensitive to surface melt.

96 5.8.2 Comparisons with previous studies

These experiments indicate that the loss of the Ross Ice Shelf is the tipping point that leads to the collapse of the WAIS under a LIG orbital conﬁguration. However, this contrasts with Sutter et al. (2016) who found that LIG WAIS collapse was initiated through the Weddell Sea sector. The Sutter et al. (2016) study applies uniform ocean temperature anomalies to present day observations, however, differences in orbital conﬁguration mean that present day ocean warming patterns are not comparable to LIG ocean warming and this could account for the contrasting outcomes. In this study, a negative temperature anomaly is generated in the Weddell Sea under all LIG σ forcings (Figure 5.3a), and this is not found in the present day record. The coarse, 40km resolution used by Sutter et al. (2016) could also have an impact on these results.

DeConto and Pollard (2016) also apply uniform temperature anomalies to present day datasets. Air and ocean temperature anomalies are applied every 5 kyrs at 130 kyrs (1.97◦ and 1.70◦), 125 kyrs (1.41◦ and 1.51◦), 120 kyrs (0.83◦ and 1.09◦), and 115 kyrs (-1.57◦ and 0.31◦). This climate forcing is applied to present day and Last Glacial Maximum (LGM) ice sheet conﬁgurations, and does not allow evolution through a glacial cycle. This presents further problems as directly applying an evolving climate to an ice sheet related to a different climate regime is physically unsound. DeConto and Pollard (2016) also introduce a cliff failure mechanism to their ice sheet model, which has been a further source of contention as this is not a process that has been observed in Antarctica. The Wilkes and Aurora Basins lose mass, whereas they remain intact under all climate scenarios in this study, however, although the climate anomalies are applied by DeConto and Pollard (2016) over a shorter timescale, they are higher than those used in this study and the warming patterns between the studies will vary.

This study has applied spatially variable climate anomalies generated by a GCM forced under a LIG orbital conﬁguration, and avoids the difﬁculties associated with transient climate forcing by implementing constant forcing. Although this method does not give a rate of change, it does give a more reliable estimate of ice sheet behaviour under a given climate regime and can be used to compare the impact of various GHG concentrations.

The use of one-way climate-ice-sheet coupling is a limitation of this study, as well as those by DeConto and Pollard (2016) and Sutter et al. (2016). Under one-way coupling, key feedbacks are omitted. This includes positive feedbacks on retreat, such as the acceleration of mass loss as a result of reduced Southern Ocean overturning caused by ocean freshening due to ice sheet retreat (Golledge et al., 2014). It also includes negative feedbacks on retreat, such as the buttressing effects of sea ice.

Peak LIG CO2levels from the ice core record range from 255 to 298 ppm (Köhler et al., 2017).

Peak LIG CO2levels in this study range from 287 to 307 ppm, capturing the higher range scenarios. The lower-estimate σ0 ice sheet response is similar to that reported Fyke et al. (2011),

97 with mass loss of only 16 cm SLE, whereas the upper-estimate ice sheet simulation under the σ10 climate scenario produces mass loss equivalent to 648 cm SLE, falling within the 6-7.5 m range of DeConto and Pollard (2016).

Ross Sea ocean temperature anomalies of only ∼0.05◦C to ∼0.4◦C were able to drive WAIS collapse in the upper and lower-estimate experiments, respectively. This study shows that ocean temperature anomalies of less than 0.5◦C are sufﬁcient to trigger WAIS collapse over 20 kyrs of forcing under a LIG orbital conﬁguration. Sutter et al. (2016) infer that the ocean temperature anomaly threshold for LIG WAIS collapse and SLR of 3-4 m was 2-3◦C, however their transient experiments were run over a shorter timescale of 13.5 kyrs. This study shows that σ6, which has a CO2e less than 7 ppm over the current upper bounds in the ice core record, has the potential to drive mass loss of over 6 m, and even σ4 can generate almost 5 m SLR.

5.9 Conclusions

This study demonstrates both that the WAIS is sensitive to small increases in GHG concentrations under a constant interglacial climate. Self-gravitational rises in local sea level in response to loss of ice sheet mass are not accounted for in this study, and so the magnitude of retreat may be underestimated in some areas. However, these results provide a valuable addition to our knowledge of Antarctic ice sheet dynamics during the LIG.

Previous attempts to model Antarctic ice sheet collapse during the LIG may have been unsuccessful due to an underestimate of GHG concentration used as input to climate models, leading to cooler ocean and air temperature outputs used to drive the ice sheet models.

WAIS collapse is driven by ocean warming and MISI also plays an important role. The Ross and Amundsen Sea sectors, which are largely grounded below sea level, are the most vulnerable, and the high altitude areas, including Marie Byrd Land and the Ellsworth Mountains remain intact.

98 Chapter 6

Climate mitigation and the stability of the West Antarctic Ice Sheet

6.1 Overview

Here, a series of highly theoretical experiments examine the response of the Antarctic ice sheets to a switch from a warm, interglacial climate with elevated GHG concentrations, to a cooler climate represented by the present day. With the recent discussion surrounding ambitious geo-engineering projects to mitigate future sea level rise (Moore et al., 2018; Irvine et al., 2018), this series of experiments investigates how the Antarctic ice sheets would respond if it were possible to reset the Earth’s climate from an warmer world, back to the present day.

Snapshots of the simulated ice sheet are taken from the σ10 forced ice sheet experiment (see Chapter 5) at 100, 200, 300, 400 and 500 years. These snapshots are used as starting conditions for a series of experiments where the interglacial forcing is removed and the cooler, control climate from the RACMO 2.3 dataset (Figure 3.5) is applied for 20 kyrs. The extent of any ice sheet recovery is then assessed.

The aim of this investigation is to test the thresholds of ice sheet recovery over a millennial timescale.

99 6.2 Interglacial perturbation

Here, the ice volume, thickness of the simulated ice sheet at each snapshot will be discussed, and the drivers of these changes will be explored in Section 6.4.

6.2.1 Ice volume response

Constant σ10 forcing over 100 years generates a small positive ice volume anomaly of 0.5 cm SLE (Figure 6.1). After 200 years, the ice sheet exhibits a negative ice volume anomaly of 6.4 cm SLE. This increases to 20.6 cm SLE after 300 years, 39.8 cm SLE after 400 years, and 57.1 cm SLE after 500 years. Unlike the grounded ice volume, the ﬂoating ice rapidly decreases from the start. At ∼130 years, the Ross Ice Shelf collapses and ﬂoating ice volume stabilises. Once the Ross Ice Shelf has been removed, the loss of grounded ice volume accelerates.

Figure 6.1: Ice volume anomaly in SLE and floating ice volume as a % of the control under constant σ10 forcing over 500 years. Blue line represents the ice volume anomaly in SLE, and the green line represents the floating ice volume. Purple shading defines Ross Ice Shelf removal. Ice volume anomalies are shown, rather than absolute values, to remove the impact of model drift.

6.3 "Recovery" simulations

The simulated ice sheets from each interglacial snapshot were used as the starting conditions for these "recovery" simulations. The control climate generated by RACMO2.3 (Figure 3.5) was applied to the ice sheets for 20 kyrs in order to determine whether the ice sheet mass lost could be regained.

100 These experiments are referred to as IS100b, IS200b,IS300b, IS400b, and IS500b according to how many years the ice sheet was perturbed by σ10 climate forcing.

6.3.1 Ice volume response

Mass loss continues after the control climate has been applied (Figure 6.2). The negative ice volume anomaly for IS100b peaks at 11 cm SLE before returning to a positive ice volume anomaly. The remaining experiments lose mass for over 2000 years after the interglacial forcing has been removed, and the volumes lost are much greater than those simulated under IS100b conditions. Under the IS200b scenario, a peak negative ice volume anomaly of 196 cm SLE is generated, under IS300b, there is a peak negative ice volume anomaly of 220 cm SLE. The IS400b scenario demonstrates the largest peak negative ice volume anomaly (229 cm SLE), and the IS500b scenario exhibits a peak negative ice volume anomaly of 123 cm SLE. Interestingly, although the IS500b experiment has experienced the longest σ10 climate forcing, it has a smaller peak negative ice volume anomaly than both the IS300b and IS400b simulations.

After 20 kyrs under control forcing, experiment IS100b is the only simulation that achieves a positive ice volume anomaly (20 cm SLE). Although there is some recovery across the 20 kyrs, these simulated ice sheets still exhibit a negative ice volume anomaly of 80-119 cm SLE.

The ice sheet in the IS200b experiment gains 116 cm SLE from peak mass loss after 20 kyrs, recovering to a negative ice volume anomaly of 196 cm SLE. IS300b recovers to a negative ice volume anomaly of 220 cm SLE, it has the highest gains over 20 kyrs of all the experiments, at 117 cm SLE. The IS400b simulation generates the highest mass loss and gains 110 cm SLE, ending with a negative ice volume anomaly of 229 cm SLE. The IS500b ice sheet gains 42 cm SLE and ends with a negative ice volume anomaly of 123 cm SLE.

101 Figure 6.2: Ice volume anomaly in SLE under constant present day climate forcing over 20 kyrs for IS100b, IS200b, IS300b, IS400b, and IS500b. Initial ice volume is taken from σ10 climate forcing snapshots at 100, 200, 300, 400 and 500 years.

6.3.2 Ice thickness change

While the interior and the norther AP gain mass under σ10 climate perturbation, the coastal areas of the Ross, Weddell, Totten and Amery all lose mass (Figure 6.3). The Ross Ice Shelf is lost after the 100 year snapshot, but the Ronne-Filchner remains intact. Unsurprisingly, following the loss of the Ross Ice Shelf, the glaciers along the Siple Coast recede the furthest.

Overall mass loss continues after the climate perturbation has been removed. The Mercer and Whillans ice streams retreat further, however, the Siple Dome gains mass, and the thickness of the Bindschadler and MacAyeal ice streams returns to the control value around the grounding line, despite upstream thinning.

102 103 Figure 6.3: Ice thickness relative to the control for: 1) 100 years, 2) 200 years, 3) 300 years, 4) 400 years, 1045) 500 years. A: Perturbed B: Recovery 6.4 Discussion

Although these experiments are not realistic, they can give insights into the ability of the WAIS to recover from partial collapse. The Siple Coast ice streams control the dynamics of much of the WAIS, and so the buttressing effect of Ross Ice Shelf is vital to the survival of the ice sheet. The basal melting of ice shelves has been highlighted as an important driver of Antarctic ice sheet loss (Pritchard et al., 2012). Under σ climate perturbation, the increased intensity of the southern westerly winds helps to drive warm CDW onto the continental shelf, which causes the break-up of the ice shelf.

The return to cooler oceans allows mass gain around the Antarctic coast, as mass loss continues in the interior of the ice sheet. Under all scenarios, except IS100b, an immediate switch to control conditions is not sufﬁcient to allow the ice sheet to return to its control state. These simulated ice sheets continue to lose mass after interglacial climate forcing is removed. Under the IS200b setup, the climate is reset at the point where the ice sheet has a negative ice volume anomaly of 6.4 cm SLE, however, this reaches 196 cm SLE under control climate forcing. The grounding line of the Whillans and Mercer Ice streams reach a pinning point and do not retreat further after 200 years.

6.4.1 WAIS inroads

The tipping-point for irreversible mass loss in this study is the breakup of the Ross Ice Shelf. This leads to retreat of the Siple Coast tributary glaciers, particularly the Whillans and Mercer ice streams, into their basins. The mass gained under the control climate is not sufﬁcient to cause grounding line re-advance. These ice streams are underlain by soft marine sediments and are "pure" ice streams, bounded by ice, rather than topographic channels, which makes them especially vulnerable to retreat (Huybers et al., 2017; Leeman et al., 2016).

Sutter et al. (2016) found that LIG WAIS collapse was initialised by removal of both the Ronne-Filchner and Ross ice shelves, and DeConto and Pollard (2016) found mass loss was driven simultaneously from the Ross, Weddell and Amundsen seas. However, different ocean forcings were applied in these studies. Future projections show destabilising of the Amundsen Sea sector, with the Bellinghausen Sea and Amundsen Sea sector ice shelves displaying the most vulnerability to reduced buttressing capability (Jenkins et al., 2010; Feldmann and Levermann, 2015; Fürst et al., 2016). Here, the Amundsen sector is insensitive to ocean forcing, and is one of the few areas that gain mass when the perturbation is removed. This area has been shown to be highly sensitive to the basal melt interpolation function (see Chapter 5), which was not used for these experiments. The ocean temperature forcings in the Amundsen and Ross sectors are comparable (Figure 5.3), which suggests the lack of response is due to the ice sheet model setup. The mass gains over the Amundsen sector are the result of relatively high precipitation rates in

105 this area, which are much higher than over the Ross sector (Figure 5.2). It is likely that the removal of the Ross ice shelf would cause precipitation rates to rise over the Siple Coast, but this cannot be captured in this study due to the one-way coupling used. Further investigation is required to explore the recovery potential of the Amundsen sector ice streams, however, their retrograde beds consisting of marine sediment are not favourable for glacial re-advance.

The Ronne-Filchner ice shelf is protected from the warm CDW by a large cold water mass on the continental shelf in the southwestern Weddell Sea (Hellmer et al., 2017; Schodlok et al., 2016; Darelius and Sallee, 2018). There are also greater topographical constraints in this basin than the Ross Sea, with Berkner Island, and the Korff and Henry Ice Rises providing pinning points for the ice shelf. The response of the Weddell Sea sector ice streams also echo the ﬁndings of Huybers et al. (2017), as they exhibit a more conservative response to forcing. Huybers et al. (2017) hypothesised that these ice streams showed potential for signiﬁcant advance under increased precipitation or modest sea level reduction, however, these results show that they do not re-advance where the climate perturbation is removed.

6.4.2 Isostatic rebound

The Siple Dome gains mass, and the Bindschadler and MacAyeal ice streams return to control ice volume, despite large areas of thinning inland and low annual precipitation (Figure 3.5b). This suggests that isostatic rebound may play a role in this area of ice sheet stabilisation.

The unloading of the underlying lithosphere as the ice sheet recedes causes both instantaneous elastic and delayed visco-elastic rebound (Peltier, 1974; Konrad et al., 2015). Instantaneous rebound has been found to delay WAIS collapse by up to 5000 years (Konrad et al., 2015).

Gomez et al. (2015) found that lower emission scenarios (2 x CO2) generate the greatest ice sheet stabilisation though bedrock uplift. The mantle beneath the WAIS has a much lower viscosity than the global average, which means that bedrock response is rapid (Barletta et al., 2018), however, under strong climate forcing, even the most responsive bed is unable to prevent WAIS collapse (Konrad et al., 2015).

Without the effects of isostasy, the ice sheet should recover linearly, however, each experiment recovers at a different rate, demonstrating the impact of isostatic rebound. Peak mass loss is not as high for the IS500b simulation as the IS400b and IS300b simulations, which have experienced shorter interglacial climate forcing. This suggests that the greater the initial mass loss, the more responsive the crust is. At the end of 20 kyrs, the IS500b ice sheet has a similar mass to the IS200b simulated ice sheet. Interestingly, the rate of ice sheet recovery is greatest under the IS300b scenario, although it is the IS400b that loses the most mass once the climate perturbation has been removed.

106 6.4.3 Conclusions

There is a ﬁne non-returnable threshold between 100 years and 200 years of constant interglacial forcing. Where the Ross Ice Shelf is lost under extreme interglacial conditions, an immediate switch to a cooler climate is not sufﬁcient to allow the WAIS to recover. Although isostatic rebound cannot prevent WAIS collapse, it can slow grounding line retreat and increase the recovery rate under some conditions.

The ice sheet continues to lose mass for over 2000 years after the switch, and only achieves small gains over 20 kyrs of constant present day climate forcing. Any climate mitigation must be made before the Antarctic ice sheet starts to respond to an extreme warming scenario.

107

Chapter 7

Conclusions

7.1 Palaeo-climate reconstruction

Elevated CO2 concentrations improve the GCM response to LIG forcing, generating a more realistic result than previous modelling studies that better compares to the palaeo-record. Under the PMIP3 CO2 concentration, AMOC is similar to the pre-industrial, there is minimal reduction in sea ice cover, and mean mid-depths ocean and surface air temperature anomalies are small. The upper-boundary of uncertainty in the ice core record, σ4, represents a tipping point where AMOC slows down. The results of this study give weight to the hypothesis that Antarctic LIG warming was driven by global ocean circulation, rather than regional forcing.

Although these experiments show that elevated CO2 concentrations lead to a better reconstruction of LIG climate, Mk3L temperature outputs are still below those found in the palaeo-record. This is most likely due to limitations of climate modelling, particularly the positioning of the Southern Hemisphere westerlies, and the lack of feedback mechanisms included in the experiments. Mk3L is known to simulate deep water that is too cold and too fresh, with a rate of AABW formation that is too strong, and a rate of NADW formation which is too weak (Phipps et al., 2011). Similar biases are found in other ocean models of a comparable complexity, however, even complex, computationally expensive climate models are unable to model critical thresholds, such as AMOC shutdown and Dansgaard–Oeschger events (Valdes, 2011). Fischer et al. (2018) highlight the lack of vital feedback mechanisms in climate models and reinforce the importance of gaining greater understanding of past warm climate intervals.

At present, only one fully-coupled climate-ocean-ice-sheet LIG simulation has been carried out (Goelzer et al., 2016). Although the inclusion of ice sheet changes improved simulations, the poor resolution of the atmospheric component presented limitations and the model could not reproduce the warming found in the proxy records. Ideally, to allow for a realistic ice sheet evolution, long term climate model simulations should be carried out that run from the glacial maximum preceding the LIG, through to the end of the LIG. However, the computational

109 expense is too high, even for models of intermediate complexity. The higher the model complexity, the shorter the time-period the experiments can cover. This means that long-term climate changes are not sufﬁciently investigated (Valdes, 2011). Greater computational resources are required to improve long-term, fully-coupled transient simulations of the LIG. Moreover, this study demonstrates that a range of GHG concentrations should also be considered in order to increase accuracy of palaeo-climate simulations.

7.2 The Cryosphere

Although they do not rule out WAIS collapse during the late LIG, Holloway et al. (2016) argue that the magnitude and spatial pattern of δ18O in the Antarctic ice core records do not indicate mass loss from the WAIS in the early LIG. Instead, they suggest that a 65±7% reduction of winter sea ice is responsible for the isotopic signal. This study ﬁnds similar levels of sea ice loss, and supports the WAIS collapse hypothesis, however it is unable to place temporal constraints on ice sheet collapse. It is also unable to reconstruct the LIG double-peak in GMSL rise that has been suggested (Kopp et al., 2009), however previous studies have found that this is not vital for reconstruction of total GMSL rise during this period (Goelzer et al., 2016).

This study shows that small increases in GHG concentration are able to dramatically increase the Antarctic contribution to sea level rise under a LIG orbital conﬁguration. Over 20 kyrs of constant forcing, ocean temperature anomalies in the Ross Sea of only ∼0.05◦C to ∼0.4◦C were able to drive WAIS collapse. The σ4 climate scenario, within the bounds of the ice core record, results in an Antarctic sea-level contribution of almost 5 m, this compares with Goelzer et al. (2016) who simulated a peak of 4.4 m using transient forcing and two-way coupling.

The results of this study emphasise the importance of both MISI and the basal melt of ice shelves to the stability of the WAIS. The areas of WAIS collapse are largely grounded below sea level, with highland areas, including Marie Byrd Land, the Ellsworth Mountains and the AP remaining intact. This compares favourably with the vulnerable areas highlighted by Bamber et al. (2007). It also supports the possibility of an open seaway between the Ross, Amundsen and Weddell Seas during the LIG, as suggested by Vaughan et al. (2011). Turney et al. (in review) have recently found evidence of open water and methane release close to the Patriot Hills in the Ellsworth Mountains during the LIG, which could also indicate WAIS collapse.

Interestingly, both the Wilkes and Aurora Basins survive the high and low estimate scenarios under all climate perturbations, despite being grounded below sea level. Mengel and Levermann (2014) suggest that these basins are vulnerable to collapse once a small proportion of ice at the front is removed. This "ice plug" remains intact for all of the simulations in this study due to a number of factors. The simulations in this study show that the highest precipitation anomalies are found here, leading to mass gain in some of the sectors in this area. There is also a negative

110 ocean temperature anomaly in front of the Wilkes Basin. The shape of the coastline and size of the continental shelf in front of the basins suggests that this area could be less susceptible to intrusions of CDW than the Ross and Amundsen Sea sectors as the ACC is deﬂected away from the coast. The present-day Cook, Ninnis and Mertz shelves at the edge of the Wilkes basin are thought to have a large passive ice shelf area, which further indicates the region could be relatively stable (Fürst et al., 2016). However, given that warming is underestimated by Mk3L, even under σ10 forcing, there is a possibility that the plug could be removed under greater forcing, destabilising the Wilkes and Aurora basins and adding 3-4 m to GMSL.

It has been noted that a paradigm shift has occurred in Antarctic ice sheet modelling in recent years (Pattyn, 2018). Increased computational efﬁciency has allowed for high spatial resolutions and improved grounding-line representation, which is a key issue in ice sheet modelling (Pattyn and Durand, 2013). The quality and quantity of basal topography data has also improved, although the wide ranging of values for basal heat ﬂux suggest that this boundary condition could be better constrained. Pollard et al. (2015) have introduced hydrofracture and ice cliff failure mechanisms to their ice sheet model in order to generate the levels of LIG melt suggested by the palaeo-sea-level record, however, the introduction of these processes is considered controversial (Fogwill et al., 2016). This study emphasises that improvements in the reconstruction of past climates need to be made because ice sheet model outputs can only be as good as their inputs.

Ice sheets have been considered passive components of the climate system (Nowicki et al., 2016), however, their impact on meridional overturning and their contribution to sea level demonstrates the need for better incorporation of the cryosphere in climate models. The Ice Sheet Model Intercomparison Project (ISMIP6) aims to advance the integration of ice sheet models within climate research (Nowicki et al., 2016). Solutions resulting from frameworks such as this, should help the continued development of improved Earth models, leading to better reconstructions of past, and better predictions of future, climate and sea level.

7.3 The future

Projected global surface air temperature changes for the end of this century range from 1.6◦C (RCP 2.6) to 4.3◦C (RCP8.5) above pre-industrial conditions, with ampliﬁcation of high-latitude temperatures leading to warming of 3◦C (RCP2.6) to ∼12◦C (RCP8.5) in the polar regions. Although the Last Interglacial cannot be considered a direct analogue for future change due to differences in orbital forcing and GHG concentrations, it can tell us how the Antarctic ice sheets respond to warmer temperatures (Fischer et al., 2018).

Constraining the WAIS contribution to sea level rise and its rate of change over this century has been identiﬁed as the highest priority in Antarctic research (Scambos et al., 2017). Good et al.

111 (2018) highlighted AMOC and ice sheets as two of the major systems that could exhibit threshold behaviour. This study has demonstrated that the thresholds in these systems are small, and the impact of crossing these thresholds can be dramatic. Projected temperature increases are higher than the forcings applied in this study, and these results indicate that centennial-scale forcing at elevated temperatures can initiate irreversible WAIS collapse, even if a method were devised in the future that could reset the Earth’s climate to present day conditions. With such large social and economic consequences of even 1-2 m SLR, it is vital that global GHG emissions are reduced sooner, rather than later.

7.4 Areas of further study

There are numerous ways that this study can be built upon. Firstly, an investigation to analyse how other GCMs respond to elevated GHG concentrations under this orbital forcing would be useful. Fully-coupled climate-ocean-ice-sheet experiments could also be introduced. It would be interesting to recreate the experiments of Goelzer et al. (2016) with the σ forcings applied in this study. Even with a poorly resolved atmospheric component, it is possible that the increased GHG concentrations would improve temperature reconstructions.

Although they are more stable than the WAIS, a potential contribution to GMSL of 3-4 m means that establishing the ocean temperature thresholds for Wilkes and Aurora Basin collapse is important. It is possible that using a fully-coupled model could tip that threshold and simulated LIG sea level could rise to 10 m, but further study is required.

Following on from the climate mitigation experiments, various cooler climates could be applied to different RCPs at different stages to thoroughly assess the effectiveness of a potential future geo-engineering project. The relative importance of MISI, isostasy, and ice shelf buttressing also warrants further investigation.

Ideally, a fully coupled, high resolution experiment should be conducted that implements transient climate forcing from the penultimate glacial maximum, through the LIG. However, the computational power required is not yet available.

Ultimately, the quality of model outputs is limited by the quality of the input data used, and so advances in palaeoclimate and palaeo-ice sheet modelling are pointless if the uncertainties in the proxy records are not reduced. Further work constraining the trace gas and isotope records contained in Antarctic ice cores is vitally important to our understanding of Antarctica during the LIG.

112 7.5 Summary

This study shows that small increases in GHG concentration are able to dramatically increase the Antarctic contribution to sea level rise under a LIG orbital conﬁguration. Increased GHG concentrations improve GCM reconstructions of LIG climate, and future studies should consider uncertainties in the ice core trace gas record when simulating past climates.

113

Appendix A

Appendix

Experiments were run with under-ice temperatures and salinity set to freezing point. As the warm water was unable to reach the grounding line, the ice sheet did not respond (see overleaf).

115

Appendix B

Appendix

Sea surface temperatures were used as ocean perturbation, instead of mid-depths ocean temperatures. SST were found to reﬂect surface air temperature. As the effects of warm water advection through deeper water currents is not felt, mass loss is limited (see overleaf).

117

Appendix C

Appendix

A matrix of sensitivity tests were performed (see overleaf). They were simply labelled w1 to w66, with the "w" to distinguish these structured experiments with initial attempts that did not use the correct ocean salinity input.

The enhancement factors (shallow ice approximation and shallow shelf approximation) were adjusted for experiments w1 to w14, and the Till Phi or Topg to Phi (yield stress) values were adjusted for experiments w15 to w66.

119 Enhancement Factors w1 w2 SIA 2 3 (grounded ice) SSA 0.5 0.5 (ice streams & shelves) Topg to Phi 15.0,40.0, 15.0,40.0, (yield stress) -300.0,700.0 -300.0,700.0 notes

Velocity change (compared to Measures)