Model study of Barents Sea Water variability 

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Remote Influence of the Barents Sea Processes

The variability in the Barents Sea has remote impacts. AW brings relatively warm water to high latitudes, an important factor in making the net annual heat lost in this region greater than annual solar heat gained (Figure 1.3). Sea water brings heat to this region because the density of water normalised by the ocean mixed layer thickness and heat capacity (3992 J k 1 K 1) is much greater than the air density normalised by the atmospheric boundary layer and smaller heat capacity of air (1005 J k 1 K 1). This means that during winter, there is a strong temperature gradient between the ocean and the atmosphere. The net heat loss from the ocean has two direct results, ramifications of these can be addressed separately and give motivation for studying this region :
1. Air in the relatively cool atmosphere is warmed.
2. The water transported to the Barents Sea loses heat, and consequently buoyancy.
First, the ocean to atmosphere heat flux is correlated with sea ice variability (Årthun et al., 2012; Screen and Simmonds, 2010b). Anomalies in ocean to atmosphere heat flux during autumn and winter cause a sequence of disturbances that propagate upwards through the layers of the atmosphere into the stratosphere (Schlichtholz, 2014; Yang et al., 2016; Petoukhov and Semenov, 2010). The exact physical processes and complex atmospheric dynamics involved are under debate among atmospheric scientists (Yang et al., 2016; Vihma, 2014). However, the warm water and low sea ice anomalies in the Barents Sea are coincident with cold surface air temperature anomalies across Europe and Asia (Blackport et al., 2019; Hoshi et al., 2019; Mori et al., 2014) (Figure 1.7). It has recently been suggested that anomalies in turbulent heat flux in the Barents Sea are more causative of the cold Eurasian winters than the sea ice itself (Blackport et al., 2019). The cold surface air temperature patterns in Asia are similar to the negative phase of the large scale weather patterns, NAO and AO (Vihma, 2014; Yang et al., 2016). Positive NAO is specifically linked to extreme wind events in the North Atlantic and north-west Europe (Yiou and Nogaj, 2004; Donat et al., 2010) suggesting these are less common with less sea ice cover. The autumn sea ice concentration anomalies show correlation with regional winter precipitation patterns over Europe and Asia with strengthening correlation since 1980 (Li and Wang, 2013). Anomalously low annual sea ice extent across the whole Arctic over recent years (Figure 1.2) has also been suggested as the cause of increased summer precipitation across northern Europe (Screen and Simmonds, 2010a). In addition, longer term changes in the summer precipitation climate could cause geopolitical tension in Asia due to water supply and usage (Bernauer and Siegfried, 2012; Zakhirova, 2013; White et al., 2014). These low sea ice years in the Barents Sea additionally push cyclonic storm tracks southward out of the Arctic Basin and further south over the Siberian coast (Inoue et al., 2012). Following the dramatic variability that may originate in the Barents Sea, this sea could be thought of as analogous to the Central Pacific for El Niño. In the case of El Niño, interannual variability causes warm sea surface temperature anomalies to spread across the Central Pacific causing atmospheric convection and anomalous weather patterns over the surrounding continents (Trenberth, 1997). Understanding the intensity and frequency of these anomalous weather events is important to improve prediction and mitigation against disruption caused by them. While the majority of the literature address this problem from an atmospheric perspective the work completed in this thesis will add analysis and discussion to this problem from an ocean perspective, a component of the dynamic system that should be better understood. Second, the ocean to atmosphere heat flux from warm, salty AW transported into the Barents Sea means it becomes more dense than the upstream AW and the relatively fresh ArW (Table 1.1) present in the Arctic Ocean (Midttun, 1985; Årthun et al., 2011). This makes the Barents Sea a region where isopycnals are ventilated and convection is likely to occur (Carmack et al., 1997). When BSW leaves the Barents Sea it sits below the fresher ArW in the Arctic Ocean, isolating it from the atmosphere (Rudels et al., 2000) (Figure 1.8).
The ventilation of isopycnals and subsequent isolation of this water mass from the atmosphere means the Barents Sea has potential to absorb and sequester carbon dioxide (Terhaar et al., 2018). Barents Sea AW has been observed and simulated to be absorbing more carbon dioxide from the atmosphere than other Arctic regions (Kaltin et al., 2002; Yasunaka et al., 2016; Terhaar et al., 2018). While the carbon dioxide absorbed here is substantial, the quantity of carbon that is advected through the Barents Sea in AW is 27 times greater (Terhaar et al., 2018; Smedsrud et al., 2013). The formation of dense BSW in the Barents Sea pushes this carbon carrying water into the Arctic Basin. Given the quantity of carbon transported through the Barents Sea, variability in the transport could affect the rate of carbon dioxide sequestering. Therefore, improving our understanding of this system could help with future climate predictions. Transport in the Barents Sea will be discussed in Chapter 4.
Once BSW enters the Arctic Basin it settles below another branch of AW entering through Fram Strait (Rudels et al., 2000). These two branches make up Arctic Intermediate Water (AIW) (Figure 1.8). AIW is important for its storage of heat below the fresher ArW layer. Mixing and heat flux between these water masses has potential to change sea ice melt rates (Polyakov et al., 2017; Rippeth et al., 2015). After circulating around the Arctic Basin, AIW exits through Fram Strait and traverses the Greenland Sea before spilling over the Denmark Strait overflow to contribute to the North Atlantic Deep Water branch of AMOC (Figure 1.3). This means the variability in BSW properties are propagated along this transport route (Karcher et al., 2011; Lique et al., 2010). AMOC is an important part of the Earth’s climate system and is dependant on the deep water formation regions to sustain the circulation. Long term (> 20 years) increases in the Arctic Ocean buoyancy through heat or freshwater fluxes could reduce AMOC transport by slowing the formation of deep water (Sévellec et al., 2017). This suggests a need to monitor and understand this transport route. How BSW density varies is a question that will be picked up in each of the results chapters of this thesis.

Seasonal and Interannual Variability of Sea Surface Tempe-rature in the Barents Sea

In this section, we characterize the temporal and spatial variations of SST over the Barents Sea. SST, by which the surface expression of PF is defined in Section 2.4, is representative of air-sea interactions that are key to the formation of BSW. We first examine the seasonal cycle because this has been suggested, from model analysis, to play an important role in BSW formation (Årthun et al., 2011; Dmitrenko et al., 2014). When averaged over the Barents Sea domain (see green box in Figure 2.1), the amplitude of the SST seasonal cycle amounts to 1.69 C, with minimum and maximum occurring in April and July, respectively. This value is large when compared to the standard deviation of the mean SST once the seasonal cycle is removed, which amounts to 0.41 C. Clearly, SST is dominated by seasonal variability. The annual winter reduction in SST is key to the formation of BSW through heat loss and given this is an annual event suggests a link between BSW and the 1 – 2.5 year residence time of AW within the Barents Sea (Smedsrud et al., 2010; Årthun et al., 2011).
Maps of seasonal mean SST, over the period 2005 – 2016 are shown in Figure 2.2a-d. It reveals a pool of warm AW in the southwestern Barents Sea with a tongue of AW in Central Basin. This warm AW tongue is intensified in winter and spring but present throughout the year. In the southwestern Barents Sea, SST increases from 4 C in spring to 8 C in summer. In the remainder of the Barents Sea, the SST also increases by 4 C between spring and summer but approaches -1.8 C in the spring due to the presence of sea ice (Figure 2.3a-d). The sea ice edge also shows strong seasonality, retreating to the northern margins of the Barents Sea in summer, while advancing towards Central Bank from the north and the south-east in winter. As discussed later in this section, the long term trend in SST changes in 2005, posing the question of a possible change of SST seasonal cycle across the full period considered. The most striking difference between the 1985 – 2005 (Figure 2.3) and the 2005 – 2016 (Figure 2.2) time periods is the location of the sea ice edge, with appreciably larger areas of open water post-2005 in all the seasons. This is accompanied by changes in SST where the seasonal sea ice has retreated.
This seasonality is primarily driven by the seasonal cycle of the net surface heat flux with a contribution from AW heat transport (Ding et al., 2016; Smedsrud et al., 2010). In the northern Barents Sea, seasonal surface heat fluxes roughly balance over a year. In contrast there is a net heat flux from ocean to atmosphere in the southern Barents Sea, suggesting the importance of heat brought here by AW for the formation of BSW (Smedsrud et al., 2010).
To examine SST variability on interannual and longer time-scales, the seasonal cycle is first removed and EOF analysis is performed (see Section 2.2 for methodology). The trend is not removed as this could be related to multidecadal variability discussed later in this section. The first mode (EOF 1) of variability in SST explains 72.9% of the variance. As the second mode explains less than 10% of the variance, we only discuss EOF 1. The spatial pattern of EOF 1 is a positive anomaly across the full Barents Sea (Figure 2.4a). PC 1 has a periodicity of 6 to 10 years but also exhibits multidecadal variability (Figure 2.4c). PC 1 is strongly correlated with the interannual variations of SAT over the Barents Sea where SAT leads by 2 months (Figure 2.4b). Regressing PC 1 on the SAT fields reveals an area of significant positive correlation over the Arctic Ocean, eastern Arctic shelf seas and northern Russia. Lag correlations with AW temperature show AW leads SST by 2 to 4 months. PC 1 is significantly correlated with the variation of AW temperature at the Kola section (r = 0.89, lag = -2 months, Figure 2.4d) and the Fugløya–Bear Island section (r = 0.80, lag = -4 months, Figure 2.4d). PC 1 is also anti-correlated with the variations of the sea ice extent in the Barents Sea (r = -0.93, lag = 1 month, Figure 2.4e).
These correlations suggest that, when mode 1 is in positive phase, SST is warm in the Barents Sea, the sub-surface AW temperature is warmer than average, sea ice extent is low and SAT is warmer than average. A mechanism that could explain this mode is an increase in the temperature of the AW inflow to the Barents Sea, which would in turn reduce sea ice extent in (b) summer (June, July and August), (c) autumn (September, October and November) and (d) winter (December, January and February), respectively. Gradient in SST seasonal climatology from 2005 to 2016 for (e) spring, (f) summer, (g) autumn and (h) winter, respectively. The sea ice edge is defined by 15% sea ice concentration (white line) and the black line indicates the 220 m isobath.
the Barents Sea, both acting to increase AW heat loss to the atmosphere (Smedsrud et al., 2010) and resulting in warmer SAT. During a positive phase of this mode, both the increase of oceanic heat lost and the decrease of the sea ice extent will most likely affect the formation of BSW, as discussed in more detail in Section 2.5.

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The Polar Front’s Constraint on the Sea Ice Edge

The magnitude of the 2D gradient in SST shows the surface manifestation of fronts in the Barents Sea (Figure 2.2e-h). Starting in the west, a front follows Spitsbergen Bank but then bifurcates at Central Bank and splits into two branches (Figure 2.2e), in agreement with the results of Oziel et al. (2016). The southern branch of this front (referred to hereafter as the Barents Sea Front) follows the western side of Central Bank southward, dividing the Barents Sea into an AW-influenced western region and a BSW-influenced eastern region. The Barents Sea Front is most prominent during winter and spring (Figure 2.2e,h) and has been discussed in greater detail by Oziel et al. (2016, 2017).
Further to the north, the PF divides the eastern Barents Sea into an ArW-influenced northern region and a BSW-influenced southern region. Our results show the PF to be a persistent feature following the 220 m isobath throughout the year, although Oziel et al. (2016) found that the PF was positioned further north than the present analysis with no fixed position. Their analysis was limited by the dataset used, comprising temperature and salinity in situ profiles collected in the Barents Sea, which captures only the sub-surface expression of the front in the 50 to 100 m depth range. SST observations reveal that the PF pathway on the east side of the Barents Sea follows the southern sides of Great Bank and Ludlov Saddle eastward to Novaya Zemlya Bank (Figure 2.2e-h, see Figure 2.1 for locations). At Novaya Zemlya Bank, the PF extends northward along Novaya Zemlya Bank to 78 N. It should be noted that a second, weaker thermal-surface front exists in the SST data due to the transition from freezing ice-covered water to warmer ice-free water. The thermal-surface front does move with the sea ice edge and sometimes coincides with the more permanent PF.
Previous studies have investigated several aspects of the PF (Våge et al., 2014; Oziel et al., 2016) but the dynamics controlling it are still poorly pinned down. Here we present some evi-dences that the PF is controlled by potential vorticity constraints. Within the Barents Sea, the PF is closely tied to the 220 m isobath (Figures 2.2 and 2.3), which is located on a steep slope separating the northern and southern Barents Sea (Figure 2.1). Potential vorticity constraints usually force currents to flow along topographic contours rather than across them (Taylor, 1917; Proudman, 1916). Planetary potential vorticity (q) can be estimated by the equation q = f =h, where f is the coriolis parameter and h is the depth. The planetary potential vorticity contours in the Barents Sea follow closely the bathymetry contours as f is roughly constant in the region. In the case of a basin with a shallower northern outflow depth than inflow i.e. a ridge, an idealised model with potential vorticity constraints drives anticyclonic/clockwise circulation around the basin and eastward along the ridge in the northern hemisphere (Yang and Price, 2000). This is consistent with the path of the PF we resolved by the OSTIA SST (Figure 2.2), as well as the eastward flow found in velocity observations on the southwestern slope of Great Bank (Våge et al., 2014) and simulations showing eastward flow along the southern slope of Great Bank (Slagstad and McClimans, 2005; Lind and Ingvaldsen, 2012).
Following Pratt (2004), additional evidence that the PF is constrained by potential vorticity can be provided by estimating the Froude number associated with the flow across the ridge towards the eastern boundary (i.e Novaya Zemlya Bank in our case). The Froude number is given by F = u=(g0d)1=2, where u is current speed, g0 is reduced gravity and d is depth of the layer at the ridge. Here we take u = 0.2 m s 1 (based on observations by Våge et al. (2014), assuming current speed is constant along the ridge), and values for g0 and d are calculated from MIMOC data (Figure 2.6), obtaining a Froude number of 0.3. Following the argument developed by Pratt (2004) and given that in our case the height of the ridge occupies roughly 1=3 of the water column, a Froude number greater than 0.2 suggests that the Great Bank–Ludlov Saddle ridge imposes a hydraulic control on the flow associated with the PF, providing further evidence that the PF is constrained by potential vorticity.
We next examine time variations of the PF, in relation to the position of the sea ice edge over time. According to Smedsrud et al. (2010), the PF sets the limit on surface area available for winter heat loss over the Barents Sea. Logically, the PF may also play a role in determining the volume of summer freshwater input from sea ice melt water. Thus the interplay between the eastern Barents Sea PF and mobile sea ice edge mediates the properties of BSW that will be carried into the Arctic as AIW. A comparison of SST gradients and sea ice concentration shows that the sea ice edge follows the inner edge of the PF in both the eastern and western Barents Sea during winter and spring from 2005 to 2016 (Figure 2.2a-d, white line) but this was not the case before 2005 (Figure 2.3, white line). Steele and Ermold (2015) suggest that during the expansion and retreat of seasonal sea ice, the edge loiters at fronts where there is a gradient in temperature inhibiting further expansion. This then implies that the expansion of sea ice south of the PF before 2005 could be consistent with cooler SST or stronger northerly winds enabling greater transport of the mobile sea ice pack across the PF enabling it to loiter closer to the Barents Sea Front.

Table of contents :

1 Introduction 
1.1 Overview
1.2 Literature Review
1.2.1 Sea ice
1.2.2 Water Masses and Circulation
1.2.3 Atmosphere
1.2.4 Arctic Ocean and Norwegian Sea
1.3 Motivation
1.3.1 Remote Influence of the Barents Sea Processes
1.3.2 Local Influence of Barents Sea Processes
1.4 Aims and Objectives
1.4.1 Thesis Structure
2 Observed atlantification of the Barents Sea 
2.1 Introduction
2.2 Data and Methods
2.2.1 Datasets
2.2.2 Methods
2.3 Seasonal and Interannual Variability of SST
2.4 The Polar Front’s Constraint on the Sea Ice Edge
2.5 Atlantification and Implications
2.6 Conclusion
3 Satellite water mass properties 
3.1 Introduction
3.2 Data and Methods
3.2.1 Data
3.2.2 Methods
3.3 Estimation of the BSW properties from satellite datasets
3.4 Understanding the variability of BSW
3.5 Conclusion
4 Model study of Barents Sea Water variability 
4.1 Introduction
4.2 Data and Methods
4.2.1 Model Simulation
4.2.2 Observational Datasets
4.2.3 Methods
4.3 Model Evaluation
4.4 Quantifying Variability in BSW Properties
4.4.1 Volume and Properties
4.4.2 Transport and Flux Budget
4.5 Notable Events in BSW Variability
4.6 The Emergence of a Regime Shift
4.7 Conclusion
5 Summary and Synthesis 
5.1 Summary
5.1.1 Chapter 2 : Observed atlantification of the Barents Sea
5.1.2 Chapter 3 : Satellite water mass properties
5.1.3 Chapter 4 : Model study of Barents Sea Water variability
5.2 Synthesis
5.3 Future Work
6 Résumé Étendu en Français 
6.1 Résumé
6.1.1 Chapitre 2 : Atlantification observée dans la Mer de Barents
6.1.2 Chapitre 3 : Caractéristiques des masses d’eau observées par satellite
6.1.3 Chapitre 4 : Etude par modélisation de la variabilité de la Mer de Barents
6.2 Synthèse
6.3 Perspectives
Bibliography 

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