Delivering sustained, coordinated and integrated observations of the Southern Ocean for global impact

The Southern Ocean is disproportionately important in its effect on the Earth system, impacting climatic, biogeochemical, and ecological systems, which makes recent observed changes to this system cause for global concern. The enhanced understanding and improvements in predictive skill needed for understanding and projecting future states of the Southern Ocean require sustained observations. Over the last decade, the Southern Ocean Observing System (SOOS) has established networks for enhancing regional coordination and research community groups to advance development of observing system capabilities. These networks support delivery of the SOOS 20-year vision, which is to develop a circumpolar system that ensures time series of key variables, and delivers the greatest impact from data to all key end-users. Although the Southern Ocean remains one of the least-observed ocean regions, enhanced international coordination and advances in autonomous platforms have resulted in progress toward sustained observations of this region. Since 2009, the Southern Ocean community has deployed over 5700 observational platforms south of 40°S. Large-scale, multi-year or sustained, multidisciplinary efforts have been supported and are now delivering observations of essential variables at space and time scales that enable assessment of changes being observed in Southern Ocean systems. The improved observational coverage, however, is predominantly for the open ocean, encompasses the summer, consists of primarily physical oceanographic variables, and covers surface to 2000 m. Significant gaps remain in observations of the ice-impacted ocean, the sea ice, depths >2000 m, the air-ocean-ice interface, biogeochemical and biological variables, and for seasons other than summer. Addressing these data gaps in a sustained way requires parallel advances in coordination networks, cyberinfrastructure and data management tools, observational platform and sensor technology, two-way platform interrogation and data-transmission technologies, modeling frameworks, intercalibration experiments, and development of internationally agreed sampling standards and requirements of key variables. This paper presents a community statement on the major scientific and observational progress of the last decade, and importantly, an assessment of key priorities for the coming decade, toward achieving the SOOS vision and delivering essential data to all end-users.Fil: Newman, Louise. University of Tasmania; AustraliaFil: Heil, Petra. Australian Antarctic Division; Australia. Antarctic Climate And Ecosystems Cooperative Research Centre; AustraliaFil: Trebilco, Rowan. Australian Antarctic Division; Australia. Antarctic Climate And Ecosystems Cooperative Research Centre; AustraliaFil: Katsumata, Katsuro. Japan Agency For Marine earth Science And Technology; JapónFil: Constable, Andrew J.. Antarctic Climate And Ecosystems Cooperative Research Centre; Australia. Australian Antarctic Division; AustraliaFil: Wijk, Esmee van. Commonwealth Scientific And Industrial Research Organization; Australia. Antarctic Climate And Ecosystems Cooperative Research Centre; AustraliaFil: Assmann, Karen. University Goteborg; SueciaFil: Beja, Joana. British Oceanographic Data Centre; AustraliaFil: Bricher, Phillippa. University of Tasmania; AustraliaFil: Coleman, Richard. University of Tasmania; AustraliaFil: Costa, Daniel. University of California; Estados UnidosFil: Diggs, Steve. University of California; Estados UnidosFil: Farneti, Riccardo. The Abdus Salam; Italia. The Abdus Salam. International Centre for Theoretical Physics; ItaliaFil: Fawcett, Sarah. University of Cape Town; SudáfricaFil: Gille, Sarah. University of California; Estados UnidosFil: Hendry, Katharine R.. University of Bristol; Reino UnidoFil: Henley, Sian F.. University of Edinburgh; Reino UnidoFil: Hofmann, Eileen. Old Dominion University; Estados UnidosFil: Maksym, Ted. University of California; Estados UnidosFil: Mazloff, Matthew. University of California; Estados UnidosFil: Meijers, Andrew J.. British Antartic Survey; Reino UnidoFil: Meredith, Michael. British Antartic Survey; Reino UnidoFil: Moreau, Sebastien. Norwegian Polar Institute; NoruegaFil: Ozsoy, Burcu. Istanbul Teknik Üniversitesi; TurquíaFil: Robertson, Robin. Xiamen University; ChinaFil: Schloss, Irene Ruth. Universidad Nacional de Tierra del Fuego; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Austral de Investigaciones Científicas; ArgentinaFil: Schofield, Oscar. State University of New Jersey; Estados UnidosFil: Shi, Jiuxin. Ocean University Of China; ChinaFil: Sikes, Elisabeth L.. State University of New Jersey; Estados UnidosFil: Smith, Inga J.. University of Otago; Nueva Zeland

Southern Ocean Action Plan (2021-2030) in support of the United Nations Decade of Ocean Science for Sustainable Development

In 2017, the United Nations proclaimed a Decade of Ocean Science for Sustainable Development (hereafter referred to as the UN Ocean Decade) from 2021 until 2030 to support efforts to reverse the cycle of decline in ocean health. To achieve this ambitious goal, this initiative aims to gather ocean stakeholders worldwide behind a common framework that will ensure ocean science can fully support countries in creating improved conditions for sustainable development of the world’s oceans. The initiative strives to strengthen the international cooperation needed to develop the scientific research and innovative technologies that can connect ocean science with the needs of society at the global scale. Based on the recommendations in the Implementation Plan of the United Nations Decade of Ocean Science for Sustainable Development (Version 2.0, July 2021), the Southern Ocean community engaged in a stakeholder - oriented process to develop the Southern Ocean Action Plan. The Southern Ocean process engaged a broad community, which includes the scientific research community, the business and industry sector, and governance and management bodies. As part of this global effort, the Southern Ocean Task Force identified the needs of the Southern Ocean community to address the challenges related to the unique environmental characteristics and governance structure of the Southern Ocean. Through this community-driven process, we identified synergies within the Southern Ocean community and beyond in order to elaborate an Action Plan that provides a framework for Southern Ocean stakeholders to formulate and develop tangible actions and deliverables that support the UN Ocean Decade vision. Through the publication of this Action Plan, the Southern Ocean Task Force aims to mobilise the Southern Ocean community and inspire all stakeholders to seek engagement and leverage opportunities to deliver innovative solutions that maintain and foster the unique conditions of the Southern Ocean. This framework provides an initial roadmap to strengthen links between science, industry and policy, as well as to encourage internationally collaborative activities in order to address existing gaps in our knowledge and data coverage

Accuracy of the classifications.

<p>The validation producer’s accuracy measures for all classes in all classifications. For the binary classifications (A), object-based models were slightly more accurate than the pixel-based classifications, and the classifications based on statistically-selected subsets of input variables produced slightly higher accuracies than those using a hypothesis-driven subset. For the ternary models (B), the overall accuracies were similar for all models, though they were more variable between classes in the object-based classifications.</p

Partial dependence plots for the ternary classification.

<p>Partial dependence plots for the moderate cover class of the pixel-based classification of <i>Azorella</i>cover based on a statistically-chosen subset of input variables. The moderate cover class was associated with high values for elevation, distance from the coast, and wind speed; with low values for the GLCM mean, wetness index, and NDVI; and with mixed values for aspect and ridgeness.</p

Binary classified map of <i>Azorella</i> distribution.

<p>Predicted <i>Azorella</i> presence on northern Macquarie Island based on binary classifications, showing (A) the entire mapping region, as predicted by hypothesis-driven pixel-based classification. Panels B–D demonstrate the variation in maps in the central part of the mapped area, as predicted by pixel-based classification of a statistically-selected subset of input variables (B); object-based classification of a statistically-selected subset of input variables (C); pixel-based classification of a hypothesis-driven subset of input variables (D); and object-based classification of a hypothesis-driven subset of input variables (E). The stippled area indicates cloud cover.</p

<i>Azorella</i> growth patterns.

<p><i>Azorella</i> exhibits a range of growth patterns on Macquarie Island, from sparse polar desert (top left) to dense herbfields (bottom right). This variability increases the challenges involved in modelling its distribution.</p

Partial dependence plots for the binary classification.

<p>Partial dependence plots for the variables selected for the pixel-based classification of Azorella presence/absence on a statistically-selected subset of input variables. The variables included were chosen on the basis of the variable importance measures. <i>Azorella</i> presence is associated with high values for elevation, distance from coast, ridgeness, and solar radiation; and with low values for GLCM mean, NDVI and red edge reflectance.</p

Ternary classified map of <i>Azorella</i> distribution.

<p>Predicted moderate (green) and sparse (orange) <i>Azorella</i> presence on northern Macquarie Island based on ternary classifications, showing the entire mapping region, as predicted by hypothesis-driven pixel-based classification (A). Panels B–D demonstrate the variation in maps in the central part of the mapped area, as predicted by pixel-based classification of a statistically-selected subset of input variables (B), object-based classification of a statistically-selected subset of input variables (C), pixel-based classification of a hypothesis-driven subset of input variables (D), and object-based classification of a hypothesis-driven subset of input variables (E). In general, the sparse class occurred on the highest and most exposed western-facing sites, and the moderate class occurred on east-facing flanks of the mountains, and in protected hollows, in line with current understanding of the species’ ecology, though the variation among the maps indicates that these classes could not be reliably distinguished from each other. In all classifications, both the moderate and sparse classes were clearly distinguished from the absent class.</p

Rapid collapse of a sub-Antarctic alpine ecosystem: the role of climate and pathogens

Ecosystem change is predicted to become more prevalent with climate change. Widespread dieback of cushion plants and bryophytes in alpine fellfield on Macquarie Island may represent such change. Loss of the keystone endemic cushion plant, Azorella macquariensis, was so severe that it has been declared critically endangered. We document the dieback and its extent. Due to the rapidity of the event, we sought to infer causes by testing two mechanistic hypotheses: (i) that extensive dieback was due to a pathogen and (ii) that dieback was a consequence of a change in climate that induced stress in several susceptible species. We searched for pathogens using both conventional and next-generation sequencing techniques. We examined plant functional morphology in conjunction with a long-term climate record of plant-relevant climate parameters to determine whether environmental conditions had become inimical for A. macquariensis. Dieback was found across the entire range of A. macquariensis. A survey found 88% of 115 stratified/ random sites contained affected cushions and 84% contained dead bryophytes. Within-site dieback increased over time. No conclusive evidence that A. macquariensis deaths were caused by a definitive disease-causing pathogen emerged. However, the presence of bacterial, fungal and oomycete taxa, some potentially pathogenic, suggested that stressed cushions could become susceptible to infection. The primary cause of collapse is suspected failure of A. macquariensis and other fellfield species to withstand recent decadal changes in summer water availability. Increased wind speed, sunshine hours and evapotranspiration resulted in accumulated deficits of plant available water spanning 17 years (1992-2008). High vulnerability to interrupted water supply was consistent with functional morphology of A. macquariensis, and climate change has altered the species' environment from wet and misty to one subject to periods of drying. Synthesis and applications. With alpine fellfield dieback baseline data on Macquarie Island established, future monitoring will determine whether this event represents a transient, decadal-level change in the ecosystem or the initiation of a climate-related, transformation away from an Azorella-dominated fellfield ecosystem. That mechanisms driving ecosystem collapse were complex and multiple stressors appeared to be impacting cumulatively may be relevant to other locations. With alpine fellfield dieback baseline data on Macquarie Island established, future monitoring will determine whether this event represents a transient, decadal-level change in the ecosystem or the initiation of a climate-related, transformation away from an Azorella-dominated fellfield ecosystem. That mechanisms driving ecosystem collapse were complex and multiple stressors appeared to be impacting cumulatively may be relevant to other locations