atmosphere

Article Characterization of Moisture Sources for Austral Seas and Relationship with Sea Ice Concentration

Michelle Simões Reboita 1, Raquel Nieto 2 , Rosmeri P. da Rocha 3, Anita Drumond 4, Marta Vázquez 2,5 and Luis Gimeno 2,*

1 Instituto de Recursos Naturais, Universidade Federal de Itajubá, Itajubá 37500-903, Minas Gerais, Brazil; [email protected] 2 Environmental Physics Laboratory (EPhysLab), CIM-UVigo, Universidade de Vigo, 32004 Ourense, Spain; [email protected] (R.N.); [email protected] (M.V.) 3 Departamento de Ciências Atmosféricas, Universidade de São Paulo, São Paulo 05508-090, Brazil; [email protected] 4 Instituto de Ciências Ambientais, Químicas e Farmacêuticas, Universidade Federal de São Paulo, Diadema 09913-030, Brazil; [email protected] 5 Instituto Dom Luiz, Universidade de Lisboa, 1749-016 Lisboa, Portugal * Correspondence: [email protected]



Received: 7 August 2019; Accepted: 12 October 2019; Published: 17 October 2019


Abstract: In this study, the moisture sources acting over each sea (Weddell, King Haakon VII, East Antarctic, Amundsen-Bellingshausen, and Ross-Amundsen) of the Southern Ocean during 1980–2015 are identified with the FLEXPART Lagrangian model and by using two approaches: backward and forward analyses. Backward analysis provides the moisture sources (positive values of Evaporation minus Precipitation, E P > 0), while forward analysis identifies the moisture sinks (E P < 0). − − The most important moisture sources for the austral seas come from midlatitude storm tracks, reaching a maximum between austral winter and spring. The maximum in moisture sinks, in general, occurs in austral end-summer/autumn. There is a negative correlation (higher with 2-months lagged) between moisture sink and sea ice concentration (SIC), indicating that an increase in the moisture sink can be associated with the decrease in the SIC. This correlation is investigated by focusing on extremes (high and low) of the moisture sink over the Weddell Sea. Periods of high (low) moisture sinks show changes in the atmospheric circulation with a consequent positive (negative) temperature anomaly contributing to decreasing (increasing) the SIC over the Weddell Sea. This study also suggests possible relationships between the positive (negative) phase of the Southern Annular Mode with the increase (decrease) in the moisture that travels from the midlatitude sources to the Weddell Sea.

Keywords: moisture sources; moisture sink; Southern Ocean Sea; sea ice concentration; Lagrangian analysis

1. Introduction Sea ice is any form of ice found at sea that has originated from the freezing of seawater [1] and it is one of the climate system components [2]. At the same time that the sea ice is a result of the interaction of the ocean-atmosphere exchanges, it also causes great influence on the Earth’s climate [3–5]. One example is the sea ice impact on the oceanic surface albedo [6], if the oceanic surface is covered by water the albedo is about 10–15% [7], while the albedo can range from 44% to 90% [8] when covered by sea ice. Studies on sea ice, in general, evaluate the extension and/or concentration of the ice. Around the Antarctic, the sea ice extends to ~19 million km2 in September and retracts to ~4 million km2 in February [9]. Therefore, from the austral winter to the beginning of spring, the area

Atmosphere 2019, 10, 627; doi:10.3390/atmos10100627 www.mdpi.com/journal/atmosphere Atmosphere 2019, 10, 627 2 of 21 covered by sea ice exceeds the area of continental ice [10]. Different from the Arctic, where a considerable decrease in sea ice extension has been observed in the last three decades, the global Southern Ocean showed a moderate increase until 2014 followed by a decline [11,12]. The long-term trend is not spatially homogeneous since it is positive over the western Ross Sea and negative on the Bellingshausen and Amundsen seas [13]. Some authors have also indicated a dominant positive trend of the sea ice concentration in the austral seas [13–16]. Sea ice extension and concentration are not only dependent on seasonality. They respond to many different interactive processes in the atmosphere and ocean [13,17], large-scale atmospheric circulation modes [18], as well as the processes associated with climate change [19]. Hobbs et al. [13] present an overview of some of the processes that change the ice extension and concentration such as the advection of heat, the horizontal movement of sea ice by wind and ocean currents, and mixed layer freshwater fluxes such as precipitation. Holland and Kwok [14] found a strong link between trends in ice concentration and ice motion (or winds) and highlighted that more southerly winds could increase the ice concentration through either increased transport of ice from the south or through an atmospheric cooling from the increased advection of cold polar air masses, while more northerly winds would decrease ice transport and cause atmospheric warming. For the Weddell Sea (which will be a focus of this study), [20,21] have shown that vigorous convective mixing, whereby the upward flux of relatively warm deep waters supplied heat, prevents sea ice formation. Gordon [21] also highlighted that during the negative phase of the Southern Annular Mode (SAM) there is a northward expansion of cold, dry polar air masses from Antarctica, which reduces precipitation and increases surface water salinity. It causes a breakdown of the pycnocline, enabling deep-reaching convection cells that transfer heat from the deep ocean into the surface layer, resulting in a winter ice-free area. On the other hand, in the case of great moisture supply (precipitation), stronger ocean stratification is expected (decrease in the mixing), with a consequent decrease in the ocean heat transport to the surface and a sea ice increase [22]. This line of reasoning also expects that more precipitation means more snow on sea ice, which further increases the ice cover due to snow-ice formation. Recent studies [23,24] have shown that northerly winds toward the Antarctic region, although transporting moist air, can contribute to the decrease in the sea ice extension and concentration through energy fluxes (vertical and meridional) as well as the movement of sea ice (e.g., [23]). In the latter case, the atmospheric circulation forces the reorganization of the sea ice drift, thus leading to more sea ice convergence (lower sea ice extent) or divergence (higher sea ice extent). Ionita et al. [17] pointed out that the negative anomalies in the sea ice concentration registered in the 2016 austral spring were associated with the enhanced poleward advection of warm and moist air, which leads to strong sea ice melting. Turner et al. [24] associated the sea ice extent and concentration anomalies of this springtime episode with a series of atmospheric circulation anomalies, from September to November 2016, such as a deeper Amundsen Sea Low (ASL) in September, which contributed to greater sea ice retreat than usual in the Weddell Sea, and the amplified planetary waves in October, which were important for the stronger meridional flow, leading to increase the heat flux poleward. A similar idea was proposed by [25], who indicated that the 10-meter wind associated with weather systems is the “chief” thermodynamic and dynamic driver of sea ice variability. However, they do not provide any information related to the meridional moisture flux associated with the weather systems. Meehl et al. [12] also studied the sudden Antarctic sea ice retreat in late 2016 using an atmosphere-only model with a specified convective heating source (to represent intensified convective activity) in the eastern Indian/western Pacific Ocean. According to Meehl et al. [12], the sea ice retreat in 2016 can be associated with 3 factors: (1) anomalous atmospheric Rossby wave with a consequent teleconnection pattern around the Antarctic, and warm-moist southward surface winds promoting sea ice convergence, (2) warmer surface water transported southward, and (3) low frequency (decadal) signal of negative wind stress trends superimposed by the positive trend of the SAM and the negative phase of Interdecadal Pacific Oscillation forced upward movement of subsurface warmer water. Atmosphere 2019, 10, 627 3 of 21

Although the teleconnection patterns appear to be important for the Southern Oceans ice retreat in spring 2016, there are climatological studies indicating that the large scale modes do not explain a large part of the sea ice variability over the whole Southern Ocean [26]. Only certain sectors of this ocean respond to different atmospheric modes [18]. Raphael and Hobbs [18] emphasize that the atmosphere-cryosphere relationship has both an immediate and a time-lagged response. The immediate response is due to the horizontal advection and vertical fluxes of energy that affect sea ice, while the time-lagged response is related to the influence of the atmosphere on the ocean, and this, through heating/cooling, would later affect the increase/decrease in the ice. For example, [27] observed that the enhancement of the zonal winds in South Pacific high latitudes during springtime allows an earlier melt of the ice in the western Ross Sea, with a consequent increase in the absorption of solar radiation by the ocean and reduced ice cover the next autumn. Many investigations of the sea ice around the Antarctic analyze the austral seas individually [6,18]. For example, sea ice in the Weddell Sea (eastern Antarctic Peninsula) is perennial, which is a result of the convergence of sea ice due to the Ekman transport associated with the Weddell Gyre [28]. Some authors [14,29,30] associated the ASL impacts on the sea ice in the Weddell Sea through the near-surface winds: north winds generally contribute to positive temperature anomalies and decreases in the sea ice concentration. Among the multiple atmospheric and oceanic drivers controlling the sea ice variability, little attention has been paid to the moisture transport from the extratropics to the Southern Ocean [23,31,32], a mechanism proposed to have an important role in the Arctic atmospheric hydrological system e.g., [33,34], with implications for the Arctic Sea ice extension. Indeed, atmospheric moisture transport is a primary source of water in polar regions, and, according to [23], through the cloud radiative forcing, it directly or indirectly affects the snow, sea ice, and ice sheet. For the first time, a study presents the climatology (for the period 1980–2015) of the main moisture sources for the Southern Ocean seas using a Lagrangian approach [35,36]. Moreover, it focuses on the relationships of the transported moisture with sea ice concentration.

2. Methodology

2.1. Data and Area of Study As previously established, one aim of this study is to find the relationship between moisture transport and sea ice concentration (SIC) over each of the austral seas. According to the National Snow & Ice Data Center [37], SIC is a unitless term describing the relative amount of area covered by ice, compared to a specific reference area, and it is typically reported as a percentage (0 to 100), a fraction (0 to 1), or sometimes in tenths (0/10 to 10/10). In these scales, 0 means that there is no ice, while a reference area completely ice-covered is represented by 1 (or 10/10 or 100). The period of study is 1980 to 2015, and the SIC is obtained from satellite using the Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager and Sounder (SSMIS) sensors on the U.S. Department of Defense Meteorological Satellite Program (DMSP) platforms. These data are processed by the National Snow and ICE Data Center from NOAA/NSIDC Climate Data Record (CDR) [38], having monthly frequency on a 0.25◦ regular horizontal resolution. ERA-Interim reanalysis from European Centre for Medium-Range Weather Forecasts (ECMWF, [39]), which is considered the state-of-the-art reanalysis in terms of the hydrological cycle [40,41], is used for the moisture sources identification and other analyses. The moisture sources identification for austral seas focuses on specific areas (which are defined in Section 2.3) of each sea around the Antarctic (Figure1). The domains defining each sea follows [ 18] and they are only used as geographic referential. Raphael and Hobbs [18] used the standard deviation of sea ice extension to specify the boundaries of the austral seas as: King Haakon VII (16◦ E–69◦ E), East Antarctic (70◦ E–162◦ E), Ross/Amundsen (163◦ E–110◦ W), Amundsen/Bellingshausen (109◦ W–68◦ W), Atmosphere 2019, 10, 627 4 of 21

Atmosphereand Weddell 2018, 9, x FOR (67 PEER◦ W–15 REVIEW◦ E). Here, for brevity, we will call the seas Ross/Amundsen4 of “Ross”,22 Amundsen/Bellingshausen “Amundsen”, and King Haakon VII “King”.

Figure 1. Topography (shaded in grayscale, m), ocean depth (shaded in blue scale, m), and boundaries Figureof the 1. Topography austral seas (shaded around Antarcticain grayscale, following m), ocean [18 depth]. (shaded in blue scale, m), and boundaries of the austral seas around Antarctica following [18]. 2.2. Lagrangian Approach for Moisture Transport Identification 2.2. Lagrangian Approach for Moisture Transport Identification The moisture transport calculation is based on the method developed by Sthol et al. [35,42], whichThe moisture uses the transport Lagrangian calculation FLEXible is PARTicle based ondispersion the method model developed (FLEXPART, by Sthol [36 et]). al. FLEXPLART[35,42], whichwas uses originally the Lagrangian developed FLEXible to study PARTicle the transport dispersion of particles, model (FLEXPART, but during [36]). recent FLEXPLART decades, it has wasbeen originally successfully developed used to to study analyze the moisturetransport transportof particles, [43 but]. The during input recent data decades, to the FLEXPART it has been is the successfullyERA-Interim used reanalysis to analyze [ 39moisture] with 61transport vertical [43]. levels The (top input of thedata atmosphere to the FLEXPART located is at the 0.1 ERA- hPa) and Interim reanalysis [39] with 61 vertical levels (top of the atmosphere located at 0.1 hPa) and interpolated to the 1◦ of horizontal resolution every 6 hours (at 00, 06, 12 and 18 UTC). Due to the interpolatedimportance to ofthe temporal 1° of horizontal resolution resolution for the accuracyevery 6 hours of the (at calculation 00, 06, 12 performedand 18 UTC). by FLEXPARTDue to the [36], importanceintermediate of temporal 3h-forecast resolution data from for the ECMWF accuracy are of also the used. calculation performed by FLEXPART [36], intermediateIn FLEXPART, 3h-forecast finite data elements from ECMWF of volume are withalso equalused. mass (hereafter “particles”) are homogeneously distributedIn FLEXPART, across thefinite atmosphere. elements Itof meansvolume that wi theth numberequal mass of “particles” (hereafter is proportional“particles”) toare the air homogeneouslydensity, and hence distributed more “particles” across the are atmosphere. initially near It the means surface. that The the forward number (or backward)of “particles” trajectories is proportionalare then calculated to the air density, using a 3Dand wind hence field more (zonal, “particles” meridional are initially and vertical near the components). surface. The forward The particles (or selectedbackward) over trajectories each area are ofstudy then calculated form part ofusing a global a 3D experimentwind field (zonal, in which meridional a total of and around vertical 2 million components). The particles selected over each area of study form part of a global experiment in which particles were globally distributed and moved for the period 1980–2015. This global experiment was a total of around 2 million particles were globally distributed and moved for the period 1980–2015. previously used in many analyses over different areas eg., [44–48] including Antarctica [32,49]. This global experiment was previously used in many analyses over different areas eg., [44–48] Once the particles and their trajectories are selected over an area of interest, the methodology including Antarctica [32,49]. developed by [35,36] is applied. In the backward mode, the trajectories of the particles indicate the Once the particles and their trajectories are selected over an area of interest, the methodology sources of moisture for a given area. The forward option computes the moisture transported from the developed by [35,36] is applied. In the backward mode, the trajectories of the particles indicate the sources into each target region (which helps to compute the moisture that forms precipitation and are sources of moisture for a given area. The forward option computes the moisture transported from called sink regions–see Section 2.4). In this study, we use both options. the sources into each target region (which helps to compute the moisture that forms precipitation and For each particle along its trajectory, the latitude, longitude, altitude, and specific humidity (q) are are called sink regions–see Section 2.4). In this study, we use both options. recorded according to the temporal resolution of the input data. The changes in moisture for each For each particle along its trajectory, the latitude, longitude, altitude, and specific humidity (q) are recorded according to the temporal resolution of the input data. The changes in moisture for each particle through the difference between the evaporation (e) and precipitation (p) are calculated for every time step. These changes in time along the individual trajectories are given by:

Atmosphere 2019, 10, 627 5 of 21 particle through the difference between the evaporation (e) and precipitation (p) are calculated for every time step. These changes in time along the individual trajectories are given by:

dq (e p) = m (1) − k dt where (e p) indicates the rate of increase (e > p) or decrease (e < p) in moisture along the trajectory of − k each particle k and m is the mass of each particle. Adding the e p in an atmospheric column over a − given area (A) for all the tracked particles residing in it, the surface freshwater flux (E–P) associated with those particles is: PK = (e p) E P = k 1 − k (2) − A where the water budget is calculated for all K particles that reside above the area A. According to [43], this methodology allows us to identify the evaporative source and sink regions for a given area (see [43] for a review on the advantages/disadvantages of different techniques to estimate moisture sources). Although the Lagrangian techniques are based on the estimation of the E P budget, they have been − also used extensively in dozens of papers with great success for the precipitation estimation through the humidity coming from the source to the target regions for global [50,51] or regional studies [45,52,53].

2.3. Backward Trajectories: Identification of the Moisture Sources for the Southern Ocean Seas As an initial step, moisture sources for the five individual Southern Ocean seas are identified using the backward trajectories. Only the areas covered by the SIC equal or greater than 0.1 (10%) are used as the target area inside every austral sea. These target areas vary monthly due to the SIC annual cycle (and other variabilities that cause changes in the SIC). With this in mind, all particles residing over each sea (the target regions) are tracked backward in time over 10 days to assess where the particles gain moisture. The period of tracking is limited to 10 days since it is the average residence time of water vapor in the atmosphere [54]. The total atmospheric moisture budget (E P) integrated over − the 10 days of tracking (i10) and averaged over the period of interest (annual or seasonal), hereafter named (E P)i10, shows where the particles acquired or lost moisture before reaching the target areas. − Where (E P)i10 > 0, the air parcels over that area gain moisture from the surrounding environment − (through evaporation from the surface to the atmosphere and also in the atmosphere due to changes in the water phase such as rain evaporating as it falls into a dry layer). Therefore, the moisture sources are characterized by (E P)i10 > 0. Annual and seasonal sources are selected, considering austral − seasons: summer as December–January–February (DJF), autumn as March–April–May (MAM), winter as June–July–August (JJA), and spring as September-October-November (SON).

2.4. Forward Trajectories: Main Moisture Sinks in the Southern Ocean Seas In order to investigate the total moisture contribution over every austral sea, the moisture sources are defined taking into account the 75th percentile of the (E P)i10 > 0 annual mean value (and it is − considered a threshold). The areas with moisture ((E P)i10 > 0) greater than this value (black contours − in Figure2) are used as the initial regions for the forward tracking. From these areas, the particles are tracked forward for 10 days and where (E P)i10 < 0, they lose moisture (i.e., through precipitation − and changes in the water phase) characterizing the moisture sink. Although the area of the SIC greater than a threshold varies monthly, the moisture contribution may affect the sea ice in different ways, leading to its formation or melting. For this reason, it is interesting not only to analyze the contribution over the total sea ice covered areas (SIC value equal to 1). In addition, since we are also interested in understanding the SIC, target areas in the forward analysis are represented by the annual maximum region with SIC values equal or greater than 0.1, which occur in September (blue areas in Figure2). Therefore, for each sea, the SIC seasonality is Atmosphere 2019, 10, 627 6 of 21 investigated in the same target area over the year. In the results section, the values of the moisture sink areAtmosphere multiplied 2018 by, 9, x 1FOR to PEER facilitate REVIEW interpretation. 7 of 22 −

Figure 2. Annual climatology of 10 days integrated (E P)i10 > 0 values (mm day 1) for the period − − 1980–2015 determined from the backward tracking. Shaded (scales in the bottom) represents moisture Figure 2. Annual climatology of 10 days integrated (E - P)i10 > 0 values (mm day−1) for the period sources and the black lines mark the threshold of the 75th percentiles of (E P)i10 > 0 for each individual 1980–2015 determined from the backward tracking. Shaded (scales in the− bottom) represents moisture a b c d e seasources - being (and) Weddell, the black ( lines) King, mark ( ) Eastthe threshold Antarctica, of (the) Amundsen75th percentiles and (of) Ross,(E - P)i10 blue > indicates 0 for each the targetindividual area on thesea forward- being ( analysis,a) Weddell, i.e., ( theb) King, area with(c) East SIC Antarctica, values equal (d or) Amundsen greater than and 0.1 (e for) Ross, September. blue Noteindicates that the the color target scale area for on the the Amundsen forward analysis, Sea diff ersi.e., fromthe area that with for theSIC othervalues seas. equal or greater than 2.5. Moisture0.1 for Sink September. and Sea Note Ice Concentrationthat the color scale for the Amundsen Sea differs from that for the other seas. TheThe relationships annual cycle between of the moisture the moisture sources sink (i.e., and the the moisture SIC are contribution explored through from the the source climatological into the annualoverlying cycle, atmosphere) trends, and theis obtain Pearsoned correlationas the average coe ffiovercient the (with area and delimited without by month-lag). the annual Details 75th aboutpercentile the lag-correlation (black lines in method Figure are2) of in the Section monthly 3.2 .valu Additionales of (E - analysisP)i10 > 0. is In carried Figure out3, the for annual the Weddell cycle Sea,of where the moisture previous sources studies haveof the proposed Ross SAUS, different the mechanismsKing ATL, and for SICthe variabilityEast Antarctica [20–22 IND]. For are this sea,characterized the periods withby strong low andseasonality, high SIC presenting are identified higher (and values considered between July to be-October extreme and events), lower ones using percentilesfrom January-March. of 20% (P20) Comparatively, and 80% (P80), from respectively, July-Octob ander greater the relationship intensity of the with moisture the moisture sources sink is is discussed.noted in the These King ATL percentiles due to both are obtained more intense from va thelues monthly and larger moisture areas of sinkthe sources (i.e., (E as shownP)i10 East Antarctica IND, and Ross IND present a higher spread of the monthly values (larger on https:differences//www.cpc.ncep.noaa.gov between 25th and 75th/products percentiles),/precip while/CWlink Amundsen/daily_ao_index SAUS and /PACaao/ monthly.aao.index.the lower spread. b79.current.asciiFigure 4 further. shows the great presence of extreme events in the moisture sources in most of the areas, with slightly higher frequency in King ATL. The Weddell ATL and Ross IND have a higher occurrence of outliers compared with the other sources.

Atmosphere 2019, 10, 627 7 of 21

3. Results

3.1. Main Features of the Moisture Sources

Backward Analysis: Identification of Moisture Sources for the Southern Ocean Seas Figure2 shows the annual climatology of the moisture sources for each austral sea, i.e., the regions presenting the atmospheric water budget values greater than zero ((E P)i10 > 0) considering 10 days − backward integration. The main moisture sources are located over the extratropical latitudes, in general, north-westward of the target areas. Therefore, the moisture sources are linked to the preferential path of the storm tracks, which is from west to east during all months of the year [31,55,56]. These sources are coherent with a number of case studies. For example, [31] showed that the strong cyclones are responsible for moisture transport toward Antarctica exceeding the mean values, with higher transport to East Antarctica and the Amundsen-Bellingshausen seas. According to Figure2, the Weddell, Ross, and Amundsen seas have two moisture sources, while King and East Antarctica have only one. The Weddell Sea receives moisture from the Pacific (PAC) and Atlantic (ATL) Oceans, the Amundsen’s moisture sources are from PAC and southern Australia (SAUS), the Ross receives moisture from the Indian Ocean (IND) and SAUS, the King’s moisture sources are from ATL, and the East Antarctica’s from IND (Figure2). It is interesting to note that for the Weddell and King Seas (Figure2a,b) there is a weak moisture source poleward of the target area (and for this reason they are not included in the following analysis), which is likely related to katabatic winds. Most of the moisture sources are more intense during austral winter-spring and weaken during autumn-summer (figures not shown). The seasonality is weaker and less evident for moisture coming from the Pacific to the Amundsen Sea. The annual cycle of the moisture sources (i.e., the moisture contribution from the source into the overlying atmosphere) is obtained as the average over the area delimited by the annual 75th percentile (black lines in Figure2) of the monthly values of (E P)i10 > 0. In Figure3, the annual cycle of the − moisture sources of the Ross SAUS, the King ATL, and the East Antarctica IND are characterized by strong seasonality, presenting higher values between July–October and lower ones from January-March. Comparatively, from July–October greater intensity of the moisture sources is noted in the King ATL due to both more intense values and larger areas of the sources as shown in Figure2. The other moisture sources (the Weddell PAC-ATL and Ross IND), although with peaks during May–October, present weak seasonality, while the two moisture sources from Amundsen are the weakest and are almost constantAtmosphere 2018 over, 9, x the FOR year PEER (REVIEWFigure 3). 8 of 22

Annual Cycle 2 Ross IND Ross SAUS Weddell ATL 1.5 Weddell PAC Amundsen PAC Amundsen SAUS King ATL 1 EA IND

E-P>0 (mm/day) 0.5

0 J F M A M J J A S O N D

Figure 3. Annual cycle of the 10 days integrated (E P)i10 > 0 values over the moisture sources (black Figure 3. Annual cycle of the 10 days integrated (E− - P)i10 > 0 values over the moisture sources (black line in Figure2) for each austral sea for the period 1980–2015. In the legend, the name of each austral line in Figure 2) for each austral sea for the period 1980–2015. In the legend, the name of each austral sea is followedsea is followed by the by name the name of the of respective the respective moisture moisture source. source. Note: Note: the the meanmean values are are obtained obtained over the areaover delimited the area by delimited the annual by the 75th annual percentile 75th percentile (black (black line inline Figure in Figure2). 2).

3.5 box central mark = median box edges = 25th and 75th percentiles 3 whiskers =most extreme data points + = outliers

2.5

2

1.5

E-P>0 (mm/day) E-P>0 1

0.5

0 Ros IND Ros SAUS Wed ATL Wed PAC Amd PAC Amd SAUS King ATL EA IND

Figure 4. Boxplot of the monthly mean moisture sources ((E - P)i10 > 0 in mm/day) for the period 1980–2015. The central red mark indicates the median, blue boxes are the 25th and 75th percentiles, black bars (whiskers) extend to the most extreme data points not considered outliers, and the outliers are plotted individually using the '+' symbol. Note: the mean values are obtained over the area delimited by the annual 75th percentile (black line in Figure 2).

3.2. Forward Analysis: Relationships Between the Moisture Sink and the SIC In the analysis of moisture sinks, for all seas with two moisture sources, the time series are added (for example, for the Ross Sea we added the IND and SAUS moisture sinks to obtain only one time series). Figure 5 shows the annual cycle of the moisture sink (moisture arriving in the target areas from source), obtained from forward trajectories ((E - P)i10 < 0), over the maximum annual area with sea ice concentration equal or greater than 0.1, which occurs in September (blue area in Figure 2). To facilitate the interpretation of the results, the moisture sink values are multiplied by -1. The moisture sink annual cycles have different patterns from those of the moisture sources (Figure 3), where the

Atmosphere 2018, 9, x FOR PEER REVIEW 8 of 22

Annual Cycle 2 Ross IND Ross SAUS Weddell ATL 1.5 Weddell PAC Amundsen PAC Amundsen SAUS King ATL 1 EA IND

E-P>0 (mm/day) 0.5

Atmosphere 2019, 10, 627 8 of 21

0 J F M A M J J A S O N D The boxplot of the mean monthly moisture sources from 1980 to 2015 is shown in Figure 4. King ATL, East Antarctica IND, and Ross IND present a higher spread of the monthly values (larger Figure 3. Annual cycle of the 10 days integrated (E - P)i10 > 0 values over the moisture sources (black differences between 25th and 75th percentiles), while Amundsen SAUS and PAC the lower spread. line in Figure 2) for each austral sea for the period 1980–2015. In the legend, the name of each austral Figure4 further shows the great presence of extreme events in the moisture sources in most of the sea is followed by the name of the respective moisture source. Note: the mean values are obtained areas, with slightly higher frequency in King ATL. The Weddell ATL and Ross IND have a higher over the area delimited by the annual 75th percentile (black line in Figure 2). occurrence of outliers compared with the other sources.

3.5 box central mark = median box edges = 25th and 75th percentiles 3 whiskers =most extreme data points + = outliers

2.5

2

1.5

E-P>0 (mm/day) E-P>0 1

0.5

0 Ros IND Ros SAUS Wed ATL Wed PAC Amd PAC Amd SAUS King ATL EA IND

Figure 4. Boxplot of the monthly mean moisture sources ((E P)i10 > 0 in mm/day) for the period − 1980–2015.Figure 4. Boxplot The central of the red monthly mark indicates mean moisture the median, source blues ((E boxes - P)i10 are > the0 in 25th mm/day) and 75th forpercentiles, the period black1980–2015. bars (whiskers) The central extend red mark to the indicates most extreme the median, data points blue boxes not considered are the 25th outliers, and 75th and thepercentiles, outliers areblack plotted bars (whiskers) individually extend using to the most ‘+’ symbol. extreme Note:data points the mean not considered values are outliers, obtained and over the the outliers area delimitedare plotted by individually the annual 75th using percentile the '+' symbol. (black line Note in: Figure the mean2). values are obtained over the area delimited by the annual 75th percentile (black line in Figure 2). 3.2. Forward Analysis: Relationships Between the Moisture Sink and the SIC 3.2. ForwardIn the analysis Analysis: of Relationships moisture sinks, Between for all the seas Moisture with two Sink moisture and the sources,SIC the time series are added (for example,In the analysis for the of Ross moisture Sea we sinks, added for all the seas IND with and two SAUS moisture moisture sources, sinks the to obtaintime series only are one added time series).(for example, Figure 5for shows the Ross the annual Sea we cycle added of thethe moistureIND and sink SAUS (moisture moisture arriving sinks to in obtain the target only areas one from time source), obtained from forward trajectories ((E P)i10 < 0), over the maximum annual area with sea ice series). Figure 5 shows the annual cycle of the− moisture sink (moisture arriving in the target areas concentrationfrom source), equalobtained or greater from forward than 0.1, trajectories which occurs ((E in- P)i10 September < 0), over (blue the area maximum in Figure annual2). To area facilitate with the interpretation of the results, the moisture sink values are multiplied by 1. The moisture sink annual sea ice concentration equal or greater than 0.1, which occurs in September− (blue area in Figure 2). To cyclesfacilitate have the di interpretationfferent patterns of the from results, those the of themoisture moisture sink sources values (Figureare multiplied3), where by the-1. The oceans moisture have maximasink annual from cycles July tohave October. differe Fornt patterns the moisture from sinks, those theof the annual moisture cycles sources are relatively (Figure flat 3), over where much the of the year, presenting weak peaks as a function of the sea. The Weddell, King, and East Antarctica seas have a weak maximum from February to April (Figure5a–c), while in the Ross Sea (Figure5e) it is displaced to April–June, and in the Amundsen Sea to August (Figure5d). On the other hand, the minimum amount of the moisture sink occurs from September to December (Figure5). In terms of the average, the moisture sink is higher and lower, respectively, in the Amundsen and Weddell seas. These annual cycle shapes of the moisture sink, with the maximum at the end-summer/autumn and the minimum in spring-begin/summer, are similar to those in the Arctic [34]. As moisture sinks over the target areas are mainly associated with precipitation, we can compare it with precipitation climatology over the Southern Ocean. In this context, the slight maximum of the moisture sink in the austral autumn in most of the austral seas (Figure5) agrees with the precipitation climatology southward of 67.5◦ S[57]. Considering more regional comparisons, the relatively weak seasonality of the moisture sink in East Antarctica (Figure5c), with a slight peak in the austral autumn months, is in agreement with observations of rainfall in the Macquarie Island located in this sea [58]. The maximum Atmosphere 2019, 10, 627 9 of 21 of the moisture sink between winter and spring in the Amundsen Sea (Figure5d) is also observed in the rainfall at the Rothera station located in the Antarctica Peninsula on the border of the Amundsen SeaAtmosphere [59]. 2018, 9, x FOR PEER REVIEW 10 of 22

a) Weddell Sea r=-0.88 d) Amundsen Sea r=0.22 2 1 4.5 1

4 1.8 0.8 0.8 3.5 1.6 0.6 3 0.6

1.4 0.4 2.5 0.4 E-P (mm/day) E-P (mm/day) 2 1.2 0.2 0.2 Sea Ice Concentration Ice Sea 1.5 Concentration Ice Sea

1 0 1 0 J F M A M J J A S O N D J F M A M J J A S O N D Months Months

b) King Haakon VII Sea r=-0.97 e) Ross Sea r=-0.54 1 3 1 3 0.8 0.8 2.5 2.5 0.6 0.6 2 2 0.4 0.4 E-P (mm/day) E-P (mm/day) 1.5 1.5 0.2 0.2 Sea Ice Concentration Ice Sea Sea Ice Concentration

1 0 1 0 J F M A M J J A S O N D J F M A M J J A S O N D Months Months

c) East Antarctica Sea r=-0.92 1 3.5

0.8 Legend 3

2.5 0.6 E-P

2 0.4

E-P (mm/day) SIC 1.5 0.2 Sea Ice Concentration Ice Sea

1 0 J F M A M J J A S O N D Months 1 FigureFigure 5.5. Annual cycle of the SIC (green line) and moisture sink (mm day−1 multipliedmultiplied by by -1, -1, blue blue line) line) inin eacheach australaustral sea:sea: (a) Weddell, (b) King Haakon VII, ( (cc)) East East Antarctica, Antarctica, ( (dd)) Amundsen, Amundsen, and and ( (ee)) Ross. Ross. TheThe errorerror barsbars showshow thethe standardstandard deviationdeviation ((σσ)) ofof thethe monthlymonthly means.means.

TheTable moisture 1. Lagged sink correlation presents coefficients strong interannual between the variability, anomalous as (without measured annual by cycle) the standard time series deviation of of thethe monthly moisture values, sink ((E in - P)i10 most < of0, but the multiplied seas (Figure by -1)5). for Moreover, each austral the sea interannual and the SIC. variability For example, of the moisturelag-0 sink (0 month is higher lag) overuses allthe monthsmoisture of sink the and year the in SIC the from Weddell, January, King lag-1 and (1-month East Antartic lag) uses and the weaker in themoisture Amundsen sink andfrom RossJanuary seas. and the SIC from February, and so on. Statistically significant values (p- Comparingvalue < 0.05) are the in annual bold. The cycles strongest of the correlation moisture ob sinktained with in each those austral of the sea SIC, is highlighted in general, in there gray. is an inverse pattern: the maximum SIC occurs when the moisture sink is weaker (Figure5). This behavior King Haakon VII East Antarctica Amundsen Ross is reflectedLags inWeddell the negative Sea and high values (from 0.54 to 0.97) of correlation coefficients (top of Sea − Sea− Sea Sea Figure5) in most of the seas, except the Amundsen Sea (0.22). In other words, the moisture seems to lag-0 −0.092 0.060 −0.070 −0.031 0.075 make a negative contribution to the SIC over the blue area displayed in Figure2. This result is similar lag-1 −0.198 −0.035 −0.112 −0.176 −0.008 to [34] for the Arctic region and the case study performed by [23,24] for the austral seas. Figure5 further showslag-2 that the SIC−0.311 in the Weddell Sea−0.127 has stronger interannual−0.212 variability−0.281 (as shown by−0.121 standard deviationlag-3 bars) from−0.240 January-June, and−0.086 equally large variability−0.107 is observed−0.126 in the Amundsen−0.141 Sea throughoutlag-4 the year.−0.168 For the Weddell− Sea,0.062 the higher interannual−0.126 variability− of0.088 the SIC is observed−0.166 in the periodlag-5 of the greater−0.084 moisture sink,−0.045 while in the Amundsen−0.025 Sea it occurs−0.077 in the periods−0.105 of both higherlag-6 and lower− moisture0.045 sink. The 0.018King and East Antarctic−0.027 seas register−0.056 greater SIC interannual−0.056 variabilityFor the in thetime months series ofincluding minimum all moisture months supplyof the year (July–December)., Figure 6 presents A more the complicated correlation pattern maps is(correlations noted in the between Ross and the Amundsen anomalous seas moisture where thesinks SIC and interannual the SIC in variabilityeach sea, which is greater, were respectively, merged to fromprovide May-October only one map) and July–December. for the lags from 0 to 5 months. Only the statistically significant values are shown. For this reason, in general, there is no information near the Antarctic coastline. As expected from Table 1, the negative correlations are dominant in almost all austral seas, and they are higher (in absolute value) in the northern sector of the sea ice edges. Considering the time lags, negative correlations increase (in absolute value) from lag-0 to lag-2, decreasing afterwards. At lag-2 the more intense negative correlations expand in the north-south direction covering a larger area compared to

Atmosphere 2019, 10, 627 10 of 21

For a better understanding the relationship between the SIC and the moisture sink, the Pearson correlation coefficient is computed considering different time lags (months), i.e., the monthly mean moisture sink time series are fixed in time, and the SIC are displaced from 1 until 6 months ahead (Table1). The correlations are calculated for the time series of the anomaly, i.e., the mean annual cycle is subtracted from the time series. The p-value (α = 0.05) is also calculated as an indication of the statistical significance of the correlations. According to Table1, the higher negative and statistically significant correlations occur at a 2-month lag, except for the 4-month lag in the Ross Sea.

Table 1. Lagged correlation coefficients between the anomalous (without annual cycle) time series of the moisture sink ((E P)i10 < 0, but multiplied by -1) for each austral sea and the SIC. For example, − lag-0 (0 month lag) uses the moisture sink and the SIC from January, lag-1 (1-month lag) uses the moisture sink from January and the SIC from February, and so on. Statistically significant values (p-value < 0.05) are in bold. The strongest correlation obtained in each austral sea is highlighted in gray.

King Haakon East Antarctica Lags Weddell Sea Amundsen Sea Ross Sea VII Sea Sea lag-0 0.092 0.060 0.070 0.031 0.075 − − − lag-1 0.198 0.035 0.112 0.176 0.008 − − − − − lag-2 0.311 0.127 0.212 0.281 0.121 − − − − − lag-3 0.240 0.086 0.107 0.126 0.141 − − − − − lag-4 0.168 0.062 0.126 0.088 0.166 − − − − − lag-5 0.084 0.045 0.025 0.077 0.105 − − − − − lag-6 0.045 0.018 0.027 0.056 0.056 − − − −

For the time series including all months of the year, Figure6 presents the correlation maps (correlations between the anomalous moisture sinks and the SIC in each sea, which were merged to provide only one map) for the lags from 0 to 5 months. Only the statistically significant values are shown. For this reason, in general, there is no information near the Antarctic coastline. As expected from Table1, the negative correlations are dominant in almost all austral seas, and they are higher (in absolute value) in the northern sector of the sea ice edges. Considering the time lags, negative correlations increase (in absolute value) from lag-0 to lag-2, decreasing afterwards. At lag-2 the more intense negative correlations expand in the north-south direction covering a larger area compared to the other lags. Figure6 also reveals a weaker but persistent positive correlation, from lag-0 to lag-2, between the moisture sink and the SIC over the northern limit of the Ross Sea. The trends of the monthly moisture sink in each austral sea and the SIC are also evaluated. For this reason, the slope of the time series is computed using Sen’s formulation [60], while its 95% statistical confidence level is provided by the nonparametric test from Man Kendall [61–63]. The trends are listed in Table2, where statistically significant values are highlighted in bold. The monthly moisture sink over the Weddell and King seas presents a positive trend, which is statistically significant only in the Weddell Sea. On the other seas, the trend is negative, and they are not statistically significant. On the other hand, there is a positive and statistically significant trend of the SIC in most austral seas, except in the Amundsen Sea, which presents a negative trend in agreement with [13]. The positive trend in the SIC is also documented in several studies [64–66]. Atmosphere 2018, 9, x FOR PEER REVIEW 11 of 22 the other lags. Figure 6 also reveals a weaker but persistent positive correlation, from lag-0 to lag-2, betweenAtmosphere the2019 moisture, 10, 627 sink and the SIC over the northern limit of the Ross Sea. 11 of 21

Figure 6. Spatial pattern of the lagged correlation coefficients between the anomalies in each austral Figuresea of the 6. Spatial moisture pattern sink ((E of theP)i10 lagged< 0, correlation but multiplied coeffi bycients -1) in between January usingthe anomalies (a) the SIC in fromeach Januaryaustral − seafor lag-0of the (0moisture month lag),sink (((Eb) the- P)i10 SIC < from 0, but February multiplied for by lag-1 -1) in (1-month January lag), using(c )(a the) the SIC SIC from from March January for forlag-2 lag-0 (2 month(0 month lag), lag), (d )(b the) the SIC SIC from from April February for lag-3 for lag-1 (3 month (1-month lag), lag), (e) the (c) SICthe SIC from from May March for lag-4 for lag-2(4 month (2 month lag), (lag),f) the (d SIC) the from SIC Junefrom forApril lag-5 for (5 lag-3 month (3 month lag). Only lag), the(e) the statistically SIC from significant May for lag-4 values (4 month(p-value lag),< 0.05) (f) the are SIC shown from in June the panels.for lag-5 (5 month lag).. Only the statistically significant values (p- value < 0.05) are shown in the panels. Table 2. Trend of the mean monthly moisture sink and the SIC for each austral sea obtained with the Thenon-parametric trends of the test monthly from Mann-Kendall moisture withsink Sen’sin each slope. austral Statistically sea and significant the SIC valuesare also at theevaluated. level of For this reason,95% are highlightedthe slope of in bold.the time series is computed using Sen’s formulation [60], while its 95% statistical confidence level Australis provided Seas by the nonparametric Moisture test from SIC Man Kendall [61–63]. The trends are listed in Table 2, where statistically significant values are highlighted in bold. The monthly Weddell 0.0004 0.0014 moisture sink over the Weddell and King seas presents a positive trend, which is statistically King Haakon VII 0.0014 0.0015 significant only in the Weddell Sea. On the other seas, the trend is negative, and they are not East Antarctica 0.0004 0.0016 statistically significant. On the other hand, there is− a positive and statistically significant trend of the SIC in most austral seas, exceptAmundsen in the Amundsen Sea,0.0006 which presents0.0012 a negative trend in agreement − − with [13]. The positive trend in Rossthe SIC is also documented 0 in several0.0016 studies [64–66].

Table 2. Trend of the mean monthly moisture sink and the SIC for each austral sea obtained with the 3.3. Extremes in the Moisture Sink for the Weddell Sea and the SIC non-parametric test from Mann-Kendall with Sen’s slope. Statistically significant values at the level ofFor 95% the are Weddell highlighted Sea, in Figure bold. 7a presents the area occupied for SIC values exceeding 0.4 for four different months: March, April, June, and September since, according to Figure5a, the SIC has its Austral Seas Moisture SIC minimum and maximum, respectively, in March and September. Higher SIC values occupy larger and Weddell 0.0004 0.0014 smaller areas, respectively, in September and March (Figure7a). King Haakon VII 0.0014 0.0015 East Antarctica −0.0004 0.0016 Amundsen −0.0006 −0.0012 Ross 0 0.0016

Atmosphere 2018, 9, x FOR PEER REVIEW 12 of 22

3.3. Extremes in the Moisture Sink for the Weddell Sea and the SIC For the Weddell Sea, Figure 7a presents the area occupied for SIC values exceeding 0.4 for four different months: March, April, June, and September since, according to Figure 5a, the SIC has its minimum and maximum, respectively, in March and September. Higher SIC values occupy larger and smaller areas, respectively, in September and March (Figure 7a). According to [29], much of the SIC and its variability in the Weddell Sea are modulated by changes in the depth and location of the ASL, which is the deepest of the three pressure centers observed around Antarctica (Figure 7b). The ASL is located in the Pacific sector of the Southern Ocean, which includes the Ross, Amundsen, and Bellingshausen seas, over the latitude band 60°–70° S. Over this region, Figure 7c indicates that ASL and northerly winds are stronger in September Atmospherecompared2019 to, 10March,, 627 while in the Weddell Sea the presence of anomalous low pressure limited to12 the of 21 north by anomalous high pressure is also registered.

FigureFigure7. 7. ForFor thethe periodperiod 1980–2015:1980–2015: ( a)) the climatology of of the the area area occupied occupied for for SIC SIC values values (from (from NOAANOAA/NSIDC)/NSIDC) exceeding exceeding 0.4 0.4 for for four four di differentfferent months: months: March, March, April, April, June June and and September September and and (b )( seab) levelsea level pressure pressure (hPa, (hPa, shaded) shaded) and windand wind at 1000 at hPa1000 (m hPa/s with(m/s scalewith inscale the in bottom) the bottom) from ERA-Interim from ERA- andInterim (c) di andfference (c) difference between between the climatology the climatology of September of Sept andember March and of March sea level of sea pressure level pressure and winds and at 1000winds hPa. at 1000 hPa.

AccordingEarlier studies to [29 ],focusing much of theon SICWeddell and its variabilitySea [20,21] in indicated the Weddell that Sea a are higher modulated moisture by changes sink in(precipitation the depth and that location reaches of the the surface) ASL, which leads is to the stronger deepest ocean of the stratification, three pressure reducing centers observedthe heat aroundtransport Antarctica from the (Figure deep ocean7b). The to the ASL surface is located within a consequent the Pacific sector increase of thein the Southern sea ice. Ocean,On the whichother includeshand, [12,23,24] the Ross, have Amundsen, shown andthat Bellingshausenthe moisture sink seas, may over thebe associated latitude band with 60 ◦northerly–70◦ S. Over winds this region,transporting Figure 7warmc indicates air from that lower ASL and latitudes northerly to An windstarctica, are stronger and, consequently, in September reducing compared the to sea March, ice whileconcentration in the Weddell and coverage. Sea the presenceTherefore, of the anomalous knowledg lowe of pressurethe forcings limited associ toated the northwith changes by anomalous in the highSIC over pressure the Weddell is also registered. Sea helps us to understand the sea ice feedback on the climate, with consequent impactsEarlier on studiesoceanic focusing water and on Weddellcirculation. Sea In [20 this,21] context, indicated for that the a Weddell higher moisture Sea, we sink investigated (precipitation the thatatmospheric reaches the patterns surface) associated leads to strongerwith low ocean (P20) stratification, and high (P80) reducing values the of heatthe transportmoisture sink from (or the deepprecipitation) ocean to the affecting surface the with SIC. a consequent increase in the sea ice. On the other hand, [12,23,24] have shownFor that the the Weddell moisture Sea, sink Figure may 8 be presents associated the withtime northerlyseries of windsthe anomaly transporting of the warmmoisture air sink from lowertogether latitudes with the to annual Antarctica, frequency and, consequently,and intensity of reducing the extreme the seamoisture ice concentration sink events (P80 and and coverage. P20). Therefore,For a moisture the knowledge sink with ofan theannual forcings mean associated value of the with ~1.4 changes mm/day in the (Figure SIC over 5a), the Weddelltime series Sea of helps the usmonthly to understand anomaly the (from sea ice−0.22 feedback to +0.35 on mm/day) the climate, indicates with consequentstrong variability impacts (Figure on oceanic 8a). In water absolute and circulation.values, the positive In this context, anomalies for are the greater Weddell than Sea, the we ne investigatedgative ones. The the atmosphericfrequency of patternsevents higher associated than withP80 lowis increasing (P20) and with high time, (P80) while values it is of decreasing the moisture for sinkevents (or lower precipitation) than P20 a(Figuresffecting the8b,d). SIC. Only the negativeFor the linear Weddell trend Sea, has Figure statistical8 presents significance the time (α series= 0.05). of In the terms anomaly of intensity of the moisture (Figure sink8c,e), together events withhigher the than annual P80 frequencyare becoming and more intensity intense, of and the extremethe trendmoisture is statistically sink significant events (P80 (Figure and P20). 8c), while Fora moisturethere is no sink statistical with an significance annual mean for value the ofweakening the ~1.4 mm of the/day events (Figure lower5a), thethan time P20. series of the monthly anomaly (from 0.22 to +0.35 mm/day) indicates strong variability (Figure8a). In absolute values, − the positive anomalies are greater than the negative ones. The frequency of events higher than P80 is increasing with time, while it is decreasing for events lower than P20 (Figure8b,d). Only the negative linear trend has statistical significance (α = 0.05). In terms of intensity (Figure8c,e), events higher than P80 are becoming more intense, and the trend is statistically significant (Figure8c), while there is no statistical significance for the weakening of the events lower than P20. We selected the events (months) identified with the percentile method to perform composites of the SIC and atmospheric variables in order to analyze the effect of the moisture sink on the SIC. We also separated the events into two periods: from June to November (maximum SIC), and from December to May (minimum SIC) and considering the lags 0, 1 and 2 months (Figure9). Events exceeding P80 contribute to the SIC decrease in both periods of the year, while the events lower than P20 are associated with an increase in the SIC (Figure9). Atmosphere 2019, 10, 627 13 of 21

Here we present the possible relationships between the SAM and the anomalous moisture sink by calculating the frequency of months with P20 and P80 occurring in each phase (Table3). P20 months usually occur during the negative SAM, with 65% (78%) of events in the period from December–May (June–November). P80 moisture sink events usually occur in the SAM positive phase, reaching 79% (64%) from December-May (June–November).

Table 3. Total and relative frequencies (values in parenthesis) of the number of months with P20 and P80 moisture sink events occurring in each phase of the SAM. The highest values are highlighted in bold.

Southern Annular Mode (SAM) Periods SAM + SAM - P20 December–May 16 (35%) 30 (65%) P20 June–November 9 (22%) 31 (78%) P80 December–May 33 (79%) 9 (21%) P80 June–November 28 (64%) 16 (36%) Atmosphere 2018, 9, x FOR PEER REVIEW 13 of 22

a) 0.4

0.3

0.2

0.1

0

(E-P)<0 (E-P)<0 (mm/day) -0.1

-0.2

-0.3 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 2013 Years b) 6 6 d)

5 5

4 4

3 3 Frequency 2 Frequency 2

1 1

0 0 1979 1982 1985 1988 1991 1994 1997 2000 2003 2006 2009 2012 2015 1979 1982 1985 1988 1991 1994 1997 2000 2003 2006 2009 2012 2015 Years Years c) e) 0.4 -0.05 0.35 P20 0.3 -0.1 Linear Trend 0.25 -0.15 0.2

0.15 Linear Trend -0.2 (E-P)<0 (mm/day) for P20 (E-P)<0 (mm/day) for P80 0.1 P80 0.05 -0.25 1979 1982 1985 1988 1991 1994 1997 2000 2003 2006 2009 2012 2015 1979 1982 1985 1988 1991 1994 1997 2000 2003 2006 2009 2012 2015 Years Years Figure 8. Time series of moisture sink (mean value over the blue area in Figure2 multiplied by 1) for Figure 8. Time series of moisture sink (mean value over the blue area in Figure 2 multiplied by -1)− for the Weddell Sea:Sea: (a) monthlymonthly anomalyanomaly (mm(mm/day),/day), ((bb,–c)c) annual annual frequency frequency and and intensity intensity (mm (mm/day)/day) of of the themoisture moisture sink sink anomaly anomaly exceeding exceeding P80, (P80,d,e) ( annuald–e) annual frequency frequency and intensity and intensity (mm/day) (mm/day) of the moistureof the moisturesink anomaly sink anomaly lower than lower P20. than P20.

We selected the events (months) identified with the percentile method to perform composites of the SIC and atmospheric variables in order to analyze the effect of the moisture sink on the SIC. We also separated the events into two periods: from June to November (maximum SIC), and from December to May (minimum SIC) and considering the lags 0, 1 and 2 months (Figure 9). Events exceeding P80 contribute to the SIC decrease in both periods of the year, while the events lower than P20 are associated with an increase in the SIC (Figure 9).

Atmosphere 2019, 10, 627 14 of 21 Atmosphere 2018, 9, x FOR PEER REVIEW 14 of 22

Figure 9. Composite anomalies of SIC for the high (P80, top) and low (P20, bottom) extreme events of the moistureFigure 9. sinkComposite for the anomalies lags 0 (a,d of,g, jSIC), 1 for (b, ethe,h, khigh) and (P80, 2 (c ,top)f,i,l) and months. low (P20, The compositesbottom) extreme are also events of separatedthe bymoisture periods sink of maximum for the lags (June-November) 0 (a,d,g,h), 1 (b and,e,h,k minimum) and 2 (c (December-May),f,i,l) months. The SIC. composites are also separated by periods of maximum (June-November) and minimum (December-May) SIC. For a more detailed analysis of the influence of the moisture sink on the SIC, the month of the year that presentedHere the we highest present frequency the possible of P80 relationships and P20 is be selected.tween the September SAM and and the April, anomalous with 10 moisture events sink each,by represent calculating the monthsthe frequency with the of highestmonths occurrences,with P20 and respectively, P80 occurring of P80 in each and P20.phase Figure (Table 10 3). P20 confirmsmonths the results usually of occur Figures during6 and9 ,the i.e., negative the increase SAM, (decrease) with 65% in the(78%) moisture of events sink in is associatedthe period from with theDecember-May SIC decrease (June–November). (increase). In addition, P80 moisture the reduction sink events of the SICusually is considerable occur in the andSAM occupies positive aphase, largerreaching area two 79% months (64%) after from the December-May month of a high (June–November). moisture sink (P80) (Figure 10). A quasi opposite change in the SIC occurs during a low moisture sink (P20). Table 3. Total and relative frequencies (values in parenthesis) of the number of months with P20 and P80 moisture sink events occurring in each phase of the SAM. The highest values are highlighted in bold.

Atmosphere 2018, 9, x FOR PEER REVIEW 15 of 22

Southern Annular Mode (SAM) Periods SAM + SAM - P20 December-May 16 (35%) 30 (65%) P20 June-November 9 (22%) 31 (78%) P80 December-May 33 (79%) 9 (21%) P80 June-November 28 (64%) 16 (36%)

For a more detailed analysis of the influence of the moisture sink on the SIC, the month of the year that presented the highest frequency of P80 and P20 is selected. September and April, with 10 events each, represent the months with the highest occurrences, respectively, of P80 and P20. Figure 10 confirms the results of Figures 6 and 9, i.e., the increase (decrease) in the moisture sink is associated with the SIC decrease (increase). In addition, the reduction of the SIC is considerable and occupies a larger area two months after the month of a high moisture sink (P80) (Figure 10). A quasi opposite Atmospherechange in2019 the, 10 SIC, 627 occurs during a low moisture sink (P20). 15 of 21

Figure 10. Composite anomalies of SIC for high (P80, top) and low (P20, bottom) monthly extremes ofFigure the moisture 10. Composite sink (vide anomalies text to of more SIC details)for high over (P80, the to Weddelp) and low Sea (P20, for the bottom) lags-0 monthly (0 month extremes lag) (a,d ), lag-1of the (1 moisture month lag) sink ( b(vide,e) and text lag-2 to more (2 months details) lag) over (c ,thef) months. WeddelTop Sea panelfor theuses lags-0 the (0 moisture month lag) sink (a from,d), Septemberlag-1 (1 month and lag) the SIC(b,e from) and September,lag-2 (2 months October, lag) and(c,f) Novembermonths. Top for, panel respectively, uses the lag-0, moisture lag-1, sink and from lag-2. BottomSeptember panel anduses the theSIC moisture from September, sink from October, April and an thed November SIC from April, for, respectively, May and June lag-0, for, respectively,lag-1, and lag-0,lag-2. lag-1,Bottom and panel lag-2. uses the moisture sink from April and the SIC from April, May and June for, respectively, lag-0, lag-1, and lag-2. Figure5 indicated that in most of the austral seas the periods with a higher moisture sink coincide with aFigure minimum 5 indicated in the SIC. that For in themost Weddell of the Sea,austral larger seas interannual the periods variability with a higher of SIC, moisture as measured sink by thecoincide standard with deviation a minimum (Figure in the6a), SIC. occurs For whenthe Wedde the moisturell Sea, larger sink isinterannual greater. In variability order to understand of SIC, as thismeasured question, by the standard composites deviation of the atmospheric (Figure 6a), variablesoccurs when and the their moisture anomalies sink (diis ffgreater.erences In between order compositesto understand and this the climatologyquestion, the for composites the period 1980–2015)of the atmospheric are calculated. variables and their anomalies (differencesIn September between climatology, composites the and southerly the climat windsology flowfor the from period the 1980–2015) Weddell Sea are to calculated. the midlatitudes (FigureIn7 Septemberb). However, climatology, for periods the of highsoutherly moisture winds sink flow (P80) from the the anomaly Weddell presents Sea to anthe inverse midlatitudes pattern in(Figure the atmospheric 7b). However, circulation, for periods i.e., of the high air moisture flows from sink the (P80) extratropics the anomaly to thepresents Weddell an inverse Sea (Figure pattern 11 ). Thisin the anomalous atmospheric circulation circulation, acts i.e., by transportingthe air flows warmfrom the air extratropics from midlatitudes to the Weddell to the Weddell Sea (Figure Sea, which 11). negativelyThis anomalous affects circulation the SIC, even acts in by the transporting presence of extremewarm air precipitation. from midlatitudes As a result to the of theWeddell anomalous Sea, circulation,which negatively a warm affects (cold) the air bubbleSIC, even develops in the overpresen thece Weddellof extreme Sea precipitatio (to the west),n. As being a result more of intense the inanomalous September circulation, (lag-0). In a thewarm midlatitudes, (cold) air bubble the anomalous develops over sea levelthe Weddell pressure Sea field (to showsthe west), strong being low (ASL intensified) and high pressures centers, respectively, westward and eastward of the Weddell Sea. These changes in the atmospheric circulation are more intense in September (lag-0 month), weakening in the following two months (Figure 11) when the SIC is decreasing (Figure 10a–c). In addition, in Figure 11d–f an anomalous convergence at low levels (1000 hPa) over the Weddell Sea is noted, indicating that the changes in the atmospheric circulation can reduce the movement (drift) of the sea ice, as mentioned by [10], and/or drive ice toward the south with the warm air advection [12]. This hinders the occurrence of the polynyas (free areas of ice), which are important to generate new sea ice and impact the SIC (later in this section more details about polynyas will be provided). AtmosphereAtmosphere2019 2018, 10,, 9 627, x FOR PEER REVIEW 17 of 1622 of 21

1 Figure 11. (a–c) Air temperature (◦C) and horizontal wind (vectors with scale in the bottom, m s− ) at Figure 11. (a–c) Air temperature (°C) and horizontal wind (vectors with scale5 in1 the bottom, m s−1) at 1000 hPa, (c–e) sea level pressure (hPa) and 1000 hPa wind divergence (x10− s− with positive/negative −5 −1 values1000 in hPa, black (c–/graye) sea lines) level pressure anomalies (hPa) for and high 1000 moisture hPa wind sink divergence composites (x10 (P80)s in with September positive/negative (lag-0) (a ,d), Octobervalues (lag-1) in black/gray (b,e) November lines) anomalies (lag-2) for (c ,highf). moisture sink composites (P80) in September (lag-0) (a– d), October (lag-1) (b–e) November (lag-2) (c–f). Table3 indicated a preference for high moisture sink events (P80) in the SAM positive phase. However, this phase is observed in only 50% of the months used in the composites in Figure 11. This explains, at least in part, why the anomalous sea level pressure pattern (Figure 11d–f) does not exactly resemble the SAM positive phase (i.e., negative anomalies of pressure around Antarctica). However, Figure 11d highlights a strong negative anomaly of pressure from the Ross Sea to the Antarctica Peninsula, which indicates the ASL intensification. The combination of stronger ASL westward and of the high-pressure anomaly over the Weddell Sea intensifies the anomalous northerly winds that transport warm air from the extratropics to the higher latitudes, with a consequent SIC decrease. A similar circulation pattern was identified by [17,23] in September 2016, the first of the 3 months of the highest extreme sea ice retreat since the observational period (1979–present) started, and this was a month with an extremely positive SAM phase (2.33). According to [12], the decrease in sea ice extension/concentration in the positive SAM phase may be a result of the strengthened circum-Antarctic westerlies, which move warm subsurface water upward in the water column due to Ekman suction. As the sub-superficial water is warmer compared with that on the surface, it contributes to a decrease in sea ice. Atmosphere 2019, 10, 627 17 of 21

For the low moisture sink events (P20, Figure 12), there is almost a reversal pattern in the anomalies compared with P80 (Figure 11). In these events, strong negative (positive) temperature anomalies predominate in the Weddell Sea (westward), while a weaker ASL is observed concomitantly with an anomalous low-pressure northward of the Weddell Sea (Figure 12). The anomalous high pressure over the western Weddell Sea and the low pressure northward generate a horizontal pressure gradient favoring the air to flow from south to north. This anomalous pattern advects cold polar air from the interior of Antarctica to the Weddell Sea. However, this circulation pattern may associate with the katabatic winds and thus explain the low level (at 1000 hPa) wind divergence in the Weddell Sea during the P20 events (Figure 12b). Katabatic winds force sea ice away from the coast of the Weddell Sea, which causes the formation of coastal polynyas [67]. On the other hand, polynyas are areas of intense heat loss to the atmosphere [68], which promote high ice production rates, but this sea ice is advected away,Atmosphere and 2018 new, 9,polynyas x FOR PEER areREVIEW created (feedback effect) [69]. Therefore, this process is important18 of 22 to increase the area and concentration of sea ice.

o 1 Figure 12. (a–c) Air temperature ( C) and horizontal wind (vectors with scale in the bottom, m s− ) at 5 1 1000Figure hPa, 12. (c– (ea)– seac) Air level temperature pressure (hPa) (oC) and and horizontal 1000 hPa windwind divergence(vectors with (x10 scale− s in− thewith bottom, positive m /snegative−1) at values1000 hPa, in black (c–e/)gray sea level lines) pressure anomalies (hPa) for and low 1000 moisture hPa wind sink divergence composites (x10 (P20)−5 s−1 in with April positive/negative (lag-0) (a,d), May (lag-1)values (b in,e )black/gray June (lag-2) lines) (c,f ).anomalies for low moisture sink composites (P20) in April (lag-0) (a–d), May (lag-1) (b–e) June (lag-2) (c–f).

4. Conclusions This study analyses the moisture transport toward the Southern Ocean (the King Haakon VII, East Antarctic, Ross/Amundsen, Weddell, Amundsen and Bellingshausen seas) for the period 1980– 2015. The FLEXPART Lagrangian model is used to identify the main moisture sources for the austral seas and the transport of moisture from the sources to the sea-ice area (with a concentration equal or greater than 0.1 in September). Two methods are employed with FLEXPART: backward and forward analyses. In the backward analysis, all particles residing over each sea (the target regions) are tracked backward in time over 10 days to assess where the particles gain moisture. Once these areas are identified, the particles are tracked forward (also for 10-days) to identify the moisture sink over each austral sea target area.

Atmosphere 2019, 10, 627 18 of 21

Another interesting aspect is that during P20 events the spatial pattern of the anomalous sea level pressure resembles the SAM negative phase, i.e., there are anomalous high and low pressures, respectively, around Antarctica and over midlatitudes. Indeed, 7 out of the 10 months used in the composites for P20 of Figure 12 occurred during the SAM negative phase. As discussed in the introduction (and also shown by [70,71]), the effect of the atmospheric variability modes on sea ice presents regional characteristics, which prevents us from extending the result for the Weddell Sea to all austral seas. In addition, Turner et al. [24] mention that in some places the SAM positive phase helps to increase the sea ice extension/concentration, while in other places it has the opposite effect.

4. Conclusions This study analyses the moisture transport toward the Southern Ocean (the King Haakon VII, East Antarctic, Ross/Amundsen, Weddell, Amundsen and Bellingshausen seas) for the period 1980–2015. The FLEXPART Lagrangian model is used to identify the main moisture sources for the austral seas and the transport of moisture from the sources to the sea-ice area (with a concentration equal or greater than 0.1 in September). Two methods are employed with FLEXPART: backward and forward analyses. In the backward analysis, all particles residing over each sea (the target regions) are tracked backward in time over 10 days to assess where the particles gain moisture. Once these areas are identified, the particles are tracked forward (also for 10-days) to identify the moisture sink over each austral sea target area. Backward analysis ((E P)i10 > 0) reveals that the most important moisture source for the austral − seas is located in the midlatitudes storm tracks region. The maximum in the annual cycle occurs between the austral winter and spring. The Weddell, Ross and Amundsen seas have two moisture sources, while the King and East Antarctica have only one. For forward trajectories ((E P)i10 < 0), the annual cycle of the moisture sinks, in general, show − the maximum at the austral end-summer/autumn and the minimum in spring-begin/summer, which is similar to that in the Arctic. Over pre-defined sea-ice areas of austral seas, negative correlations between the moisture sink and the SIC are found, which could indicate a contribution of moisture sink to the SIC deacreases. The negative correlations are higher with two-months lagged, i.e., the greater decrease (increase) in the SIC would occur two months after the peaks (lows) in the moisture sinks. As previous studies have proposed different mechanisms for SIC variability in the Weddell Sea, additional analyses are conduced to understand the relationship between the moisture sink and the SIC in this sea. The moisture sink extremes (low and high values defined as, respectively, 20th and 80th percentiles) and the associated atmospheric circulation are discussed. We found that under low moisture sink events (20th percentile) the SIC increases since the anomalous southerly winds follow from Antarctica to midlatitudes, contributing to transport colder air from the interior of the continent to the Weddell Sea. On the other hand, during the high moisture sink events (80th percentile), anomalous northerly winds flow from midlatitudes to the Weddell Sea, with the SIC decreasing as a function of the warm air advection. This study also suggests possible relationships of the phases of the SAM, with extremes in the moisture sink in the Weddell Sea. High (low) moisture sink periods usually occur during the positive (negative) SAM phase. We suggest additional studies to evaluate whether the results obtained for the relationships between the moisture and the SIC in the Weddell Sea may occur also in the other austral seas.

Author Contributions: Conceptualization, M.S.R., L.G. and R.P.d.R.; Methodology, M.S.R., L.G. and R.N.; Software, R.P.d.R., M.S.R., M.V. and A.D.; Validation, R.N., M.S.R., R.P.d.R., A.D. and L.G.; Formal Analysis, M.S.R. and M.S.R.; Writing—Original Draft Preparation, M.S.R., R.N. and R.P.d.R.; Writing—Review & Editing, M.S.R. and R.P.d.R.; Visualization, R.N.; Supervision, L.G. and R.P.d.R.; Project Administration, R.P.d.R.; Funding Acquisition, R.P.d.R. Funding: This research was funded by FAPESP grant number 2017/03981-3, CNPq grant number 304949/2018-3, Xunta de Galicia grant number ED413C 2017/64 “Programa de Consolidación e Estructuración de Unidades de Investigación Competitivas (Grupos de Referencia Competitiva)” co-funded by the European Regional Development Fund (FEDER). M.V was supported by the Xunta de Galicia under grant ED481B 2018/062. Atmosphere 2019, 10, 627 19 of 21

Acknowledgments: The authors would like to thank NOAA and ECMWF for providing the data for this study. Conflicts of Interest: The authors declare no conflict of interest.

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