The Role of Large-Scale Drivers in the Amundsen Sea Low Variability and Associated Changes in Water Isotopes From the Roosevelt Island Ice Core, Antarctica

doi:10.21203/rs.3.rs-1215704/v1

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The Role of Large-Scale Drivers in the Amundsen Sea Low Variability and Associated Changes in Water Isotopes From the Roosevelt Island Ice Core, Antarctica

https://doi.org/10.21203/rs.3.rs-1215704/v1

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Here we examine the water stable-isotope data from the Roosevelt Island Climate Evolution (RICE) ice core. Roosevelt Island is an independent ice rise located at the northeastern margin of the Ross Ice Shelf. In this study, we use empirical orthogonal function (EOF) analysis to investigate the relationship between RICE ice-core oxygen-18 isotopes (δ¹⁸O) and Southern Hemisphere atmospheric circulation during the extended austral winter (April–November). The RICE δ¹⁸O record is correlated with Southern Annular Mode (SAM) and Pacific–South American pattern 1 (PSA1), which both project onto the Amundsen–Bellingshausen Sea (ABS) geopotential height field. Pacific sector Southern Ocean, eastern Ross Sea, and West Antarctic’s atmospheric circulation, sea ice, and surface air temperature (SAT) anomalies, as well as RICE δ¹⁸O, are strongest when El Niño–Southern Oscillation (ENSO) and SAM are “in-phase”. That is when the SAM−/PSA1+ (El Niño) and SAM+/PSA1− (La Niña) phasing prevails. When in-phase, the δ¹⁸O correlation with the 500-hPa geopotential height (Z500) is strong in regions (e.g., the Amundsen Sea) where their anomalies associated with SAM and PSA1 show the same sign. SAM−/PSA1+ (El Niño) and SAM+/PSA1− (La Niña) is associated with positive and negative δ¹⁸O anomalies, respectively. RICE δ¹⁸O can aid in establishing past natural variability of the strength of the SH high-latitude Pacific sector ENSO-SAM connection and associated atmospheric circulation, SIC, and SAT extremes.

Climate Analysis and Modeling

Climatology

Climate variability

patterns of variability

Amundsen Sea Low

Southern Annular Mode

Pacific–South American patterns

the Antarctic sea-ice dipole

ice cores

water stable isotopes

The Roosevelt Island Climate Evolution (RICE) ice-core drill site was located at the summit of Roosevelt Island (79.362°S, 161.698°W, 550 m a.s.l.). Roosevelt Island is a grounded coastal ice rise, located at the northeastern margin of the Ross Ice Shelf (Fig. 1). An intermediate-depth ice core (764 m) was drilled here over two southern summers (2011/2012 and 2012/2013) (Tuohy et al. 2015; Emanuelsson 2016; Bertler et al. 2018). Traditionally, ice-core oxygen-18 (δ¹⁸O) and deuterium (δD) water stable isotopes have been used as palaeothermometers, i.e., proxies for reconstruction of past core site air temperatures (e.g., Dansgaard 1964; Masson-Delmotte et al. 2008; Stenni et al. 2017). Atmospheric circulation (Küttel et al. 2012), sea ice extent (Bromwich and Weaver 1983; Noone and Simmonds 2004; Küttel et al. 2012), and precipitation amount (Noone and Simmonds 2002) all affect the isotopic variability in Antarctic snow and ice. Variability and trends in West Antarctic SAT (Steig et al. 2009; Schneider et al. 2012) and water isotopes (Steig et al. 2013; Sinclair et al. 2014) have been linked to changes in atmospheric circulation and sea ice extent.

Southern Hemisphere (SH) extra-tropical circulation variability is to a large extent modulated by the Southern Annular Mode (SAM) and by Rossby wave propagation (called teleconnections) associated with the El Niño–Southern Oscillation (ENSO) and with the Indian Ocean Dipole (IOD) (Lim and Hendon 2017). SAM is the leading pattern of the large-scale atmospheric variability in the SH and has a strong impact on high latitude regions (Rogers and van Loon 1982; Thompson and Wallace 2000; Ding et al. 2012). SAM is characterized by a quasi-zonally symmetric pattern with geopotential height anomalies of opposite signs over the mid-latitudes and high-latitudes (positive SAM is associated with negative anomalies over Antarctica). The variability in the strength and location of the persistent circumpolar westerly winds surrounding Antarctica is closely linked to SAM. The Pacific–South American patterns (PSA1 and PSA2) are characterized by SH Rossby wave activity and are commonly defined as the second and third empirical orthogonal functions (EOFs) of the SH extratropical geopotential height field (Kidson 1988; Karoly 1989; Mo and Higgins 1998; Marshall and Thompson 2016). PSA1 is ENSO-related, and some studies have suggested that it can be viewed as an extension of ENSO into the SH (Karoly 1989). SAM and the PSA1 pattern both project onto the geopotential height field in the Amundsen–Bellingshausen Sea (ABS) region (Turner et al. 2013; Yu et al. 2015), affecting the state of the Amundsen Sea Low (ASL). The ASL is a marked climatological low-pressure center in the ABS/Ross Sea region that is present in annual and seasonal means of mean sea-level pressure (Turner et al. 2013; Raphael et al. 2015). The phasing of SAM and PSA1 can be in-phase or out of phase and act together to reinforce or cancel geopotential height anomalies, respectively, in the ASL region (Fogt et al. 2011; Yu et al. 2015). The PSA1 teleconnection with SH high-latitudes is strong during austral winter and spring (Turner 2004; Jin and Kirtman 2009). During the austral summer season, the SH circulation pattern is different from the rest of the year with a less pronounced ENSO high-latitude influence (Turner 2004).

Atmospheric circulation can cause both wind-driven and temperature-driven changes to sea ice, which in turn may affect δ¹⁸O. Further, Antarctic sea ice changes can affect atmospheric circulation and SAT (England et al. 2018). We focus on the extended-winter season (April–November) on the 500-hPa geopotential height (Z500, to represent large-scale atmospheric circulation), SAT and sea ice concentration (SIC) in our analysis to advance our understanding of their relation and impact on δ¹⁸O.

2.1 Reanalysis and observational data

We use monthly Z500, SAT and 850-hPa meridional (V850) and zonal (U850) wind data from the European Center for Medium-Range Weather Forecasts (ECMWF) Interim Reanalysis (ERA-Interim) dataset (Dee et al. 2011). ERA-Interim data (0.75° x 0.75°) are available for the 1979–2019 period. The ERA-Interim data are considered the most reliable reanalysis products for the Antarctic climate (Bracegirdle and Marshall 2012; Jones et al. 2016). Since this research started, ERA5 reanalysis datasets have superseded the ERA-Interim. It is outside of the scope of the study to update all the analyses and the difference for this type of large-scale analysis is likely to be marginal. We use SIC from the Hadley Centre Sea Ice and Sea Surface Temperature datasets (HadISST v. 1.1) (Rayner et al. 2003). The monthly data (1.0° x 1.0°) is available from 1870 to the present. Observations from several sources are used to constrain the SIC dataset, e.g., sea ice charts and passive microwave satellite measurements. We limit our analysis to the common period between the RICE δ¹⁸O record and the satellite era, 1979–2011. For our analysis of the RICE δ¹⁸O, we compute extended-winter (April–November (inclusive, here and for other extended-winter season parameters below)) seasonal averages from monthly ERA-Interim and HadISST data. The ERA-Interim fields are downscaled to a 1.0° x 1.0° grid to get the same resolution of the datasets.

2.2 Correlations, trends, and statistical analysis

All data are detrended before linear Pearson’s correlation coefficients (r) are calculated. The significance level (p) of the correlations accounts for lag-1 autocorrelation by reducing the number of degrees of freedom (Bretherton et al. 1999). For significance levels of the correlations in Table 1 were assessed relative to synthetic noise from 1000 simulations with the same power spectra as the real data (Ebisuzaki 1997). Linear trends and regressions are calculated using the method of least squares and their statistical significance is determined using t statistics and accounting for serial autocorrelation. For the composite analysis the significance of the difference between SAM−/PSA1+ and SAM+/PSA− states were determined using a two-sample t-test.

The significant tests are rigorous with both adjustments for temporal and spatial autocorrelation. We employ the false discovery rate (FDR) test to determine field significance (Wilks 2006, 2017) for composite differences, correlation and regression patterns. We set the α_FDR to 0.1 (this level is also applied by e.g., Westra et al. (2015)), as this level approximately equates to a global significance level of 0.05 (α_FDR = 2 α_global)(Wilks 2017).

2.3 Empirical orthogonal functions

Patterns of variability examined here were calculated using EOF analysis (Storch and Zwiers 1999; Deser et al. 2010) of the extended-winter (April–November) averaged monthly ERA-Interim Z500 anomaly field (20°–90°S) over the 1979–2011 period. Prior to the EOF analysis, (1) the seasonal cycle is removed at each grid point by subtracting the corresponding monthly climatology, (2) the data is weighted using the square root of the cosine of latitude to provide equal weighting of equal areas, and (3) the linear trend is removed.

The ten first EOFs are determined and Varimax rotated. Rotation of EOFs geoscience data is recommended (Richman 1986). Only the two leading PCs are used. The two leading patterns of Z500 variability (20°–90°S; the SAM and PSA1 PCs regressed on to the Z500 anomaly field) are shown in Figures 2a and 2b. They explain 34% and 20% of the Z500 variability, respectively. PSA2 explains 9.8% of the total variance and the North et al. (1982) test shows that SAM, PSA1, and PSA2 are well separated (Fig. S1).

The extended-winter season was chosen as the season of the study because PSA1 teleconnection with SH high-latitudes is strong during winter and spring (Turner 2004; Jin and Kirtman 2009). More specifically the April–November season was selected as the δ¹⁸O correlation with PSA1 was strong and the leading EOF patterns were well-separable for this season. This seasonal circulation change in the ENSO teleconnection between the extended-winter season and the summer season is evident in the annual δD correlation with the seasonal Z500 fields (Emanuelsson 2016), see their figure 4.10. However, broadly, the same results are obtained if annual means are employed, just with lower significance (not shown).

2.4 The RICE water stable-isotope records

The δ¹⁸O record used in this study consists of data combined from the RICE 2012/13 B firn core (~1981.5–2011; 12.30–0.53 m depth; 79.362°S, 161.698°W, 550 m a.s.l.) and the uppermost part of the RICE Deep ice core (1979–~1981.5; 13.43–12.30 m depth; 79.364°S, 161.706°W, 550 m a.s.l.). The 12/13 B firn core was drilled 93 m from the main RICE drill site. The RICE δ¹⁸O and δD records were measured on a continuous-flow analysis (CFA) setup using a Los Gatos Research (LGR) Isotope Water Analyzer (IWA-35EP) (Emanuelsson et al. 2015). The age scale (depth-age relationship) used in this study was established by Winstrup et al. (2017). We use the δ¹⁸O in the subsequent analysis (Fig. 3a). However, our results are not dependent on whether δ¹⁸O or δD is used. An extended-winter δ¹⁸O record was created by averaging monthly means for δ¹⁸O and subsequently averaging over the April–November period. The monthly means are determined by linear interpolation between the age markers. However, the boundaries are approximate as we cannot claim monthly resolution of the δ¹⁸O record. A discussion of molecular diffusion’s impact on the isotope record is provided in the supplemental material (Sect. S1).

2.5 Computational procedure

The analysis and visualization of the results were done in MATLAB 2020b. The analysis code is presented at the following Github page (https://github.com/demanuelsson). The ECMWF ERA-Interim (Dee et al. 2011) (http://apps.ecmwf.int/archive-catalogue/) and the HadISST SIC (Rayner et al. 2003) (http://www.metoffice.gov.uk/hadobs/hadisst/) datasets were accessed online.

3.1 The SAM-PSA1 in-phase relationship and RICE δ¹⁸O

Strong positive δ¹⁸O correlations are found with the ASL (r_max = 0.52, p < 0.01, 1979–2011; Figs. 3b–c), with high Z500 being associated with high δ¹⁸O and low Z500 with low δ¹⁸O. Indicating that, atmospheric circulation in the ABS/Ross Sea Z500 (namely the ASL), is the driving force governing the extended-winter RICE isotopic signal (Fig. 3c). This is also reflected in the SAT and SIC correlation patterns (Figs. 3d, e). Positive δ¹⁸O anomalies are associated with both positive SAT and negative SIC anomalies in the eastern Ross Sea (Figs. 3d, e). This pattern corresponds to the western flank of the anticyclonic circulation, where strong poleward meridional winds prevail. Resulting in airmasses that are isotopically enriched by the nearby open ocean north of the sea ice edge. Changes in sea ice also modify the sensible heat flux (the conductive heat flux from the ocean to the atmosphere) (Noone and Simmonds 2004). For example, when sea ice recedes (mechanically by wind and from melting associated with warm air mass intrusions), ice is replaced by open ocean leading to an increase in sensible heat flux followed by higher SAT.

As both SAM and PSA1 project onto the ABS Z500 field (Turner et al. 2013), we next examine whether SAM and PSA1’s phase relationship is preserved in δ¹⁸O. The difference between SAM+/PSA1− and SAM−/PSA1+ years (marked by asterisk and circle in Fig. 4a, respectively) for Z500, SAT, and SIC is shown in Figure 4b–d. There is a striking similarity between the δ¹⁸O correlation panels (Figs. 3c–e) and the in-phase composite differences (Figs. 4b–d). These results show that δ¹⁸O captures the SAM PSA1 in-phase relationship during the satellite era (Figs. 3c and 4b) as well as that the associated δ¹⁸O SAT and SIC patterns (see the Figs. 3d, 4c and 3e, 4d pattern pairs). Here positive δ¹⁸O anomalies are associated with SAM−/PSA1+ and negative δ¹⁸O anomalies are associated with SAM+/PSA1− (Figs. 3a and 4a). The 2010 SAM+/PSA1− event was an exception; it was associated with a positive δ¹⁸O anomaly value.

In addition to the δ¹⁸O-Z500 correlation, we depict regions where the SAM and PSA1 overlap in Figure 3c. The significant areas (encircled by black dashed contours in Figs. 2a and 2b) are used to find regions of overlap. Here, the overlaid contours enclose regions where both patterns are active (significant at the p < 0.05 level). The contours in Figure 3c depict regions impacted by both the SAM and PSA1 modes, with the magenta contours showing regions where both modes display the same sign when the modes are in-phase (or opposing signs when out of phase). Note that SAM and PSA1 are in-phase [SAM−/PSA1+ (El Niño) or SAM+/PSA1− (La Niña)] when they are associated with the same sign of the Z500 anomaly over the Amundsen Sea (Figs. 2a and 2b). This is consistent with Z500 PC1 (SAM) being negatively correlated and Z500 PC2 (PSA1) being positively correlated with δ¹⁸O (Table 1). Positive geopotential height anomalies over the Amundsen Sea/the eastern Ross Sea region [weak ASL and positive δ¹⁸O anomaly (Emanuelsson et al. 2018)] tend to be associated with SAM’s negative phase and/or the PSA1 patterns positive phase (Fig. 3c). The region of SAM PSA1 overlap in the ABS/Ross Sea largely corresponds to the ASL region, as defined by Hosking et al. (2013) (60°–75°S, 170°E–70°W). Further evidence that the characteristic Rossby wave teleconnection pattern in the Pacific and Atlantic sectors is strengthened when SAM and PSA1 are in-phase (Fig. 3c).

Regions of significant δ¹⁸O-Z500 correlations are characterized by either: (1) a region where both EOF patterns are significant and strengthen the correlation by showing the same anomaly sign when in-phase., e.g., over the Ross and ABS (magenta contours, Fig. 3c); or (2) only one pattern is active (non-stippled and non-contoured areas), e.g., SAM over East Antarctica.

Furthermore, we have demonstrated that the EOF patterns SAM and PSA1 (that, are independent of RICE δ¹⁸O) can explain the δ¹⁸O-Z500 correlation pattern (see the overlain contours in Figure 3c). Suggesting that the leading EOF patterns are real distinct large-scale dynamical modes and not degenerates, as some studies have cautioned (Dommenget and Latif 2002; Monahan et al. 2009).

The extended-winter RICE δ¹⁸O-SIC correlation pattern shows high significance in the Amundsen/eastern Ross Seas region (Fig. 3e). The ABS SIC physically affects δ¹⁸O, by dictating the distance to the open ocean. The positive correlation with SIC in the Bellingshausen Sea and the Weddell Sea reflects the Antarctic Dipole Pattern (ADP) (Yuan and Martinson 2000; Renwick 2002; Yuan 2004; Turner et al. 2009; Thomas and Abram 2016), caused by the ASL creating opposing SIC anomalies between the Pacific and Atlantic sectors.

3.2 Decadal-scale variability

Next, we examine the temporal variability of the relationship between Z500 and SIC with δ¹⁸O for the extended-winter period. Figure 5a shows the 11-year running correlation between δ¹⁸O and the Z500 PCs. The δ¹⁸O-SAM correlation is consistently negative until the end of the correlation interval (middle year 2000) when the correlation strength is dramatically reduced. The δ¹⁸O-PSA1 correlation is stronger than the SAM correlation. The moving δ¹⁸O-PSA1 correlation is significant at the p<0.05 level until ~1999 (middle year 2005).

The effective sample size (n_eff) is larger than the sample size for the δ¹⁸O-SAM correlation (Table 1a). This occurs when the lag-one autocorrelation coefficient of one time series is negative, which is indicative of a blue noise process. We investigate this further by calculating moving average n_eff values for the δ¹⁸O correlation with SAM and PSA1 (Figs. S3a, b). The 11-year mowing window δ¹⁸O-SAM n_eff values become larger than the sample size around the year 2000 (Fig. S3a). This appears to be related to an increased magnitude towards higher frequencies in the SAM PC (Figs. S3c). A similar but subdued increase in n_eff towards more recent years is also apparent for the δ¹⁸O PSA1 correlation (Fig. S3a). Higher frequency blue noise can occur in δ¹⁸O records at shallow depths, where the signal has not yet been attenuated by diffusion (Fisher 1985; Fisher et al. 1996). This is ruled out for RI δ¹⁸O because it shows low magnitudes of higher-frequency variability close to the snow surface (Fig. S3b). We conclude that the 1990–1991 δ¹⁸O increase in magnitude during the extended-winter period is driven by the ASL (Fig. 3b).

The loss in correlation corresponds to an apparent breakdown of the in-phase relationship with δ¹⁸O (Figs. 3a and 4a). In 2010 a SAM+/PSA− (La Niña) event is associated with a positive δ¹⁸O anomaly. Additionally, PSA1 is neutral in 2011, yet δ¹⁸O displays the most positive anomaly during the satellite era. Thus, the running correlation between δ¹⁸O and SAM and between δ¹⁸O and PSA1 is lost. As the PSA1 teleconnection is related to ENSO, the running correlation with Niño-4 SST is also lost (Figs. 5d). Furthermore, as SAM and PSA1 are the dominant drivers of ADP SIC variability the δ¹⁸O running correlation with ADP is lost. Note, the δ¹⁸O moving correlation with Niño-4, PSA1, and ADP curves are almost identical. The 11-year mowing window δ¹⁸O-Niño-4 and δ¹⁸O-ADP n_eff values are smaller than the sample size for the (middle-year) 1995–2004 period (Fig. S3f), indicative of red noise.

The scatter plots in Figure 5 show the linear regression between δ¹⁸O and the two leading Z500 PCs (Figs. 5b and 5c) and with ADP and central tropical Pacific SSTs (Niño-4; Figs. 5e and 5f). If years 2010 and 2011 are excluded, the δ¹⁸O correlations with SAM and PSA1 remain strong (Figs. 5b, c). Suggesting conditions during 2010–2011 are causing a reduction in correlation strength. The loss of correlation is also evident in the spatial correlation patterns, which bear an even clearer resemblance with the Z500, SAT, and SIC in-phase composites when the correlation interval is limited to 1979–2009 (Figs. S4 and 4b–d).

The change in intercept (but not slope) implies that SAM and PSA1 still have the same effect on δ¹⁸O during the extended winter of 2010 and 2011 (red asterixis Figs. 5b, c), but a polynya and/or decadal variability (IPO, change in teleconnection) may have caused the offset in the intercept (red dashed lines in Figs. 5b, c). Polynyas and open ocean adjacent to RICE can provide a source of local maritime air, which can enrich the isotopic signal. This will be a topic for future research.

In this study, we demonstrate that for the extended-winter period from April to November, the Roosevelt Island δ¹⁸O record is correlated with the two leading modes of Southern Hemisphere atmospheric circulation variability, the SAM and PSA1. Importantly, the δ¹⁸O captures changes in the phase relationship between these two modes, which has been suggested as the driver for recent climate change in West Antarctic and the Antarctic Peninsula (Fogt et al. 2011; Clem and Fogt 2013). For example, the acceleration in West Antarctic snowfall since the 1990s has been attributed to the in-phase relationship of SAM and ENSO (Thomas et al. 2015). At Roosevelt Island, the in-phase relationship is associated with strong atmospheric, sea ice, and SAT anomalies across the Pacific sector of the Southern Ocean, the eastern Ross Sea, and West Antarctica. SAM−/PSA1+ (El Niño) is associated with positive δ¹⁸O anomalies while SAM+/PSA1− (La Niña) is associated with negative δ¹⁸O anomalies. RICE δ¹⁸O captures the ENSO-SAM relationship in the Pacific sector of the Southern Ocean and the associated atmospheric circulation, SIC, and SAT extremes. Thus, RICE δ¹⁸O can be utilized to reconstruct the strength of the ENSO-SAM connection beyond the instrumental period. Potentially to 2,700 years before present (700 B.C.E.), the limit to which the annual-resolved layer counting of the RICE core extend (Winstrup et al. 2017).

Acknowledgements

We like to acknowledge two anonymous reviewers for their constructive comments, which helped to improve the manuscript. We are also grateful to Christo Buizert who provided comments on an earlier version of this manuscript. This work is a contribution to the Roosevelt Island Climate Evolution (RICE) program. Funding for the RICE project was provided by the New Zealand Ministry of Business, Innovation, and Employment Grants through Victoria University of Wellington (RDF-VUW-1103, 15-VUW-131) and GNS Science (Global Change Through Time Programme). The authors have no conflict of interest to declare.

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You are reading this latest preprint version

The Role of Large-Scale Drivers in the Amundsen Sea Low Variability and Associated Changes in Water Isotopes From the Roosevelt Island Ice Core, Antarctica

Status:

Version 1

Abstract

Figures

1 Introduction

2 Data And Methods

2.1 Reanalysis and observational data

2.2 Correlations, trends, and statistical analysis

2.3 Empirical orthogonal functions

2.4 The RICE water stable-isotope records

2.5 Computational procedure

3 Results And Discussion

3.1 The SAM-PSA1 in-phase relationship and RICE δ¹⁸O

3.2 Decadal-scale variability

4 Summary

Declarations

Acknowledgements

References

Tables

Supplementary Files

Status:

Version 1

The Role of Large-Scale Drivers in the Amundsen Sea Low Variability and Associated Changes in Water Isotopes From the Roosevelt Island Ice Core, Antarctica

Status:

Version 1

Abstract

Figures

1 Introduction

2 Data And Methods

2.1 Reanalysis and observational data

2.2 Correlations, trends, and statistical analysis

2.3 Empirical orthogonal functions

2.4 The RICE water stable-isotope records

2.5 Computational procedure

3 Results And Discussion

3.1 The SAM-PSA1 in-phase relationship and RICE δ18O

3.2 Decadal-scale variability

4 Summary

Declarations

Acknowledgements

References

Tables

Supplementary Files

Status:

Version 1

3.1 The SAM-PSA1 in-phase relationship and RICE δ¹⁸O