Envisioning a Global Multi-Purpose Ocean Acoustic Network

Acoustics play a central role in humankind’s interactions with the ocean and the life within. Passive listening to ocean “soundscapes” informs us about the physical and bio-acoustic environment from earthquakes to communication between fish. Active acoustic probing of the environment informs us about ocean topography, currents and temperature, and abundance and type of marine life vital to fisheries and biodiversity related interests. The two together in a multi-purpose network can lead to discovery and improve understanding of ocean ecosystem health and biodiversity, climate variability and change, and marine hazards and maritime safety. Passive acoustic monitoring (PAM) of sound generated and utilized by marine life as well as other natural (wind, rain, ice, seismics) and anthropogenic (shipping, surveys) sources, has dramatically increased worldwide to enhance understanding of ecological processes. Characterizing ocean soundscapes (the levels and frequency of sound over time and space, and the sources contributing to the sound field), temporal trends in ocean sound at different frequencies, distribution and abundance of marine species that vocalize, and distribution and amount of human activities that generate sound in the sea, all require passive acoustic systems. Acoustic receivers are now routinely acquiring data on a global scale, e.g., Comprehensive Nuclear-Test-Ban Treaty Organization International Monitoring System hydroacoustic arrays, various regional integrated ocean observing systems, and some profiling floats. Judiciously placed low-frequency acoustic sources transmitting to globally distributed PAM and other systems provide: (1) high temporal resolution measurements of large-scale ocean temperature/heat content variability, taking advantage of the inherent integrating nature of acoustic travel-time data using tomography; and (2) acoustic positioning (“underwater GPS”) and communication services enabling basin-scale undersea navigation and management of floats, gliders, and AUVs. This will be especially valuable in polar regions with ice cover. Routine deployment of sources during repeat global-scale hydrographic ship surveys would provide high spatial coverage snapshots of ocean temperatures. To fully exploit the PAM systems, precise timing and positioning need to be broadly implemented. Ocean sound is now a mature Global Ocean Observing System (GOOS) “essential ocean variable,” which is one crucial step toward providing a fully integrated global multi-purpose ocean acoustic observing system.

Sea surface temperature (SST) is a fundamental physical variable for understanding, quantifying and predicting complex interactions between the ocean and the atmosphere. Such processes determine how heat from the sun is redistributed across the global oceans, directly impacting large- and small-scale weather and climate patterns. The provision of daily maps of global SST for operational systems, climate modelling and the broader scientific community is now a mature and sustained service coordinated by the Group for High Resolution Sea Surface Temperature (GHRSST) and the CEOS SST Virtual Constellation (CEOS SST-VC). Data streams are shared, indexed, processed, quality controlled, analyzed, and documented within a Regional/Global Task Sharing (R/GTS) framework, which is implemented internationally in a distributed manner. Products rely on a combination of low-Earth orbit infrared and microwave satellite imagery, geostationary orbit infrared satellite imagery, and in situ data from moored and drifting buoys, Argo floats, and a suite of independent, fully characterized and traceable in situ measurements for product validation (Fiducial Reference Measurements, FRM). Research and development continues to tackle problems such as instrument calibration, algorithm development, diurnal variability, derivation of high-quality skin and depth temperatures, and areas of specific interest such as the high latitudes and coastal areas. In this white paper, we review progress versus the challenges we set out 10 years ago in a previous paper, highlight remaining and new research and development challenges for the next 10 years (such as the need for sustained continuity of passive microwave SST using a 6.9 GHz channel), and conclude with needs to achieve an integrated global high-resolution SST observing system, with focus on satellite observations exploited in conjunction with in situ SSTs. The paper directly relates to the theme of Data Information Systems and also contributes to Ocean Observing Governance and Ocean Technology and Networks within the OceanObs2019 objectives. Applications of SST contribute to all the seven societal benefits, covering Discovery; Ecosystem Health & Biodiversity; Climate Variability & Change; Water, Food, & Energy Security; Pollution & Human Health; Hazards and Maritime Safety; and the Blue Economy.

Application of the Ocean Health Index to assess ocean and coastal conditions of the Tokyo Metropolitan area

Over the past decade, long‐term deployments of passive acoustic recorders have provided a new baseline on the seasonal occurrence of large whale species in remote regions of the world ocean. In the Arctic, passive acoustic sampling has identified both whale calls and sounds from anthropogenic sources (ships and seismic profiling), activities that are expected to increase with diminished sea ice cover. In 2008, NOAA capitalized on an opportunity to join on‐going IPY projects by inclusion of recorders at three High Arctic mooring sites: one on the Chukchi Plateau and two on either side of Fram Strait. The recorders (AURAL‐M2) provided a year of sub‐sampled (9 min on/11 min off) recordings at 0.1 Hz to 4 kHz (8192 sampling rate), which encompasses the bandwidth of whale and ice seal calls. Data from the recorders were complemented by a suite of standard oceanographic measures from other instruments on the mooring line. Provisional results show novel occurrence of both marine mammal and anthropogenic signals in the High Arctic. To realize the vision of a Global Ocean Acoustic Observing Network [Dushaw et al. (2009)], passive acoustic technology must become a standard sampling component, especially in the Arctic during this time of rapid climate change.

The “Measuring the Pulse of Earth's Global Ocean” project will attempt to quantify sound levels in what should be the quietest parts of the ocean—the five deepest locations—to gauge the baseline level of sound in these remote ocean areas. Ocean noise from human-made sources of sound (e.g., shipping) can have a detrimental effect on marine animals that use sound to sense their environment. Thus noise can negatively impact the health of marine ecosystems, which are the basis for many sectors of the global “Blue” economy, including commercial fisheries and aquaculture. This project will gather unique baseline data to monitor the “acoustic health” of the oceans. A novel, deep-ocean capable hydrophone-lander system will be deployed at each of these five deep-sea sites (all >7 km deep). The project will involve the collaboration of several U.S. governmental agencies, private industry and NGOs, and international partners to access these global locations. We will collaborate with scientists from each of these five sites to deploy, recover, and analyze this deep ocean acoustic data. Our ocean sound program aspires to develop a robust and inclusive education/outreach program, focusing on the impact of underwater noise on the health of marine ecosystems.

Since surface layer salinity (SLS) mainly reflects sea surface freshwater flux and affects ocean circulation and upper ocean stratification by modifying seawater density, knowledge of SLS variability is important to better understand the global hydrological cycle and ocean and climate variability. The global pattern of SLS interannual variability has not received as much attention compared to the seasonal variability. The SLS interannual variability in the global ocean based on Argo profiling float data over 2003–2020 was classified into seven individual clusters. SLS interannual variability was found to be regionally different even within each ocean basin and primarily corresponded to large‐scale climate mode variability. The spatial distribution of the classified SLS clusters provides the first map of SLS interannual variability in the global ocean that largely reflects the variability of the global hydrological cycle.

The rough sea bottom has a large effect on underwater acoustic propagation and underwater acoustic detection applications. By using the typical shallow water environment from the Yellow Sea, the acoustic propagation characteristics under the condition of both periodic rough sea bottom and strong negative thermocline layer are systematically analyzed by using the parabolic equation model RAM (where RAM stands for range-dependent acoustic model) and ray theory. For a low-frequency and short-range acoustic source, the transmission loss (TL) increases up to about 5–30 dB due to the existence of the periodic rough bottom. Abnormal TLs and pulse arrival structures with different source depths, different periods and heights of the rough bottom are analyzed and summarized. Specifically, when the period of the rough bottom is constant, TL increases with the height of the rough bottom increasing. When the height of the rough bottom is constant, the effect of the rough bottom on the sound propagation becomes smaller with the increase of the period. The mechanism of the TL difference caused by rough bottom is explained by using the ray theory. The incidence and reflection angle of the sound ray on the sea bottom are changed due to the periodic rough bottom, which makes small grazing angles of some of the rays incident at sea bottom become large grazing angles, and the bottom loss increases. On the other hand, the change of the reflection angle increases the number of ray interaction with the sea bottom, causing the reversion propagation. Therefore, the energy of the sound field will attenuate with range increasing. The influence of the periodic rough bottom on the sound pulse propagation is mainly reflected in the energy conversion between sound rays (or normal modes) with different angles, the increasing of energy attenuation of some sound rays with large angles, and the decreasing of multipath structure. The change of the arrival time and relative amplitude of the multipath structure affect the frequency spectrum of the sound field, which will affect the performance of the method based on matching field localization. Most of existing studies focus on the influence of the change in large scale sea bottom topography on the sound field, but there are few studies on small scale periodic sea bottom fluctuations, and the relevant summary of the law of sound propagation is lacking. When sonar is used in the actual shallow water environment, more attention should be paid to the influence of the periodic rough bottom. In addition, the present research results also have important reference significance for the spatial accuracy of surveying and mapping of sea bottom topography.

Climate variability has distinct spatial patterns with the strongest signal of sea surface temperature (SST) variance residing in the tropical Pacific. This interannual climate phenomenon, the El Niño-Southern Oscillation (ENSO), impacts weather patterns across the globe via atmospheric teleconnections. Pronounced SST variability, albeit of smaller amplitude, also exists in the other tropical basins as well as in the extratropical regions. To improve our physical understanding of internal climate variability across the global oceans, we here make the case for a conceptual model hierarchy that captures the essence of observed SST variability from subseasonal to decadal timescales. The building blocks consist of the classic stochastic climate model formulated by Klaus Hasselmann, a deterministic low-order model for ENSO variability, and the effect of the seasonal cycle on both of these models. This model hierarchy allows us to trace the impacts of seasonal processes on the statistics of observed and simulated climate variability. One of the important outcomes of ENSO’s interaction with the seasonal cycle is the generation of a frequency cascade leading to deterministic climate variability on a wide range of timescales, including the near-annual ENSO Combination Mode. Using the aforementioned building blocks, we arrive at a succinct conceptual model that delineates ENSO’s ubiquitous climate impacts and allows us to revisit ENSO’s observed statistical relationships with other coherent spatio-temporal patterns of climate variability—so called empirical modes of variability. We demonstrate the importance of correctly accounting for different seasonal phasing in the linear growth/damping rates of different climate phenomena, as well as the seasonal phasing of ENSO teleconnections and of atmospheric noise forcings. We discuss how previously some of ENSO’s relationships with other modes of variability have been misinterpreted due to non-intuitive seasonal cycle effects on both power spectra and lead/lag correlations. Furthermore, it is evident that ENSO’s impacts on climate variability outside the tropical Pacific are oftentimes larger than previously recognized and that accurately accounting for them has important implications. For instance, it has been shown that improved seasonal prediction skill can be achieved in the Indian Ocean by fully accounting for ENSO’s seasonally modulated and temporally integrated remote impacts. These results move us to refocus our attention to the tropical Pacific for understanding global patterns of climate variability and their predictability.

Marine animals equipped with biological and physical electronic sensors have produced long-term data streams on key marine environmental variables, hydrography, animal behavior and ecology. These data are an essential component of the Global Ocean Observing System (GOOS). The Animal Borne Ocean Sensors (AniBOS) network aims to coordinate the long-term collection and delivery of marine data streams, providing a complementary capability to other GOOS networks that monitor Essential Ocean Variables (EOVs), essential climate variables (ECVs) and essential biodiversity variables (EBVs). AniBOS augments observations of temperature and salinity within the upper ocean, in areas that are under-sampled, providing information that is urgently needed for an improved understanding of climate and ocean variability and for forecasting. Additionally, measurements of chlorophyll fluorescence and dissolved oxygen concentrations are emerging. The observations AniBOS provides are used widely across the research, modeling and operational oceanographic communities. High latitude, shallow coastal shelves and tropical seas have historically been sampled poorly with traditional observing platforms for many reasons including sea ice presence, limited satellite coverage and logistical costs. Animal-borne sensors are helping to fill that gap by collecting and transmitting in near real time an average of 500 temperature-salinity-depth profiles per animal annually and, when instruments are recovered (∼30% of instruments deployed annually, n = 103 ± 34), up to 1,000 profiles per month in these regions. Increased observations from under-sampled regions greatly improve the accuracy and confidence in estimates of ocean state and improve studies of climate variability by delivering data that refine climate prediction estimates at regional and global scales. The GOOS Observations Coordination Group (OCG) reviews, advises on and coordinates activities across the global ocean observing networks to strengthen the effective implementation of the system. AniBOS was formally recognized in 2020 as a GOOS network. This improves our ability to observe the ocean’s structure and animals that live in them more comprehensively, concomitantly improving our understanding of global ocean and climate processes for societal benefit consistent with the UN Sustainability Goals 13 and 14: Climate and Life below Water. Working within the GOOS OCG framework ensures that AniBOS is an essential component of an integrated Global Ocean Observing System.

Monitoring ocean acoustic noise has been the subject of considerable recent study, motivated by the desire to assess the impact of anthropogenic noise on marine life. A combination of measuring ocean sound using an acoustic sensor network and modelling sources of sound and sound propagation has been proposed as an approach to estimating the acoustic noise map within a region of interest. However, strategies for developing a monitoring network are not well established. In this paper, considerations for designing a network are investigated using a simulated scenario based on the measurement of sound from ships in a shipping lane. Using models for the sources of the sound and for sound propagation, a noise map is calculated and measurements of the noise map by a sensor network within the region of interest are simulated. A compressive sensing algorithm, which exploits the sparsity of the representation of the noise map in terms of the sources, is used to estimate the locations and levels of the sources and thence the entire noise map within the region of interest. It is shown that although the spatial resolution to which the sound sources can be identified is generally limited, estimates of aggregated measures of the noise map can be obtained that are more reliable compared with those provided by other approaches.

Adjusting xylem anatomy and growth to inter-annual climate variability in two Fabaceae species (Centrolobium microchaete, Cenostigma pluviosum) from Bolivian dry tropical forests

Climate trends and variability at adaptation scale: Patterns and perceptions in an agricultural region of the Ethiopian Highlands

The one global ocean is receiving unprecedented attention recently, due to numerous factors such as the ocean’s role in climate change and the burgeoning blue economy. However, even though the ocean allows life on this planet, very little attention has been given to the complex relationship between the ocean and human health (OHH). USA and Europe have for a number of years focused funding on OHH as a meta-discipline (i.e., a discipline that includes the ideas and perspectives of a number of other disciplines), but Canada until now has not. This article examines the advancement of OHH and argues that Atlantic Canada could and should lead a Canadian meta-disciplinary program on the ocean and human health.Keywords: global ocean, human health, meta-discipline

As the health of the global oceans continues to decline, biodiversity is severely threatened, and human well-being is challenged. The United Nations Decade of Ocean Science for Sustainable Development (referred to as "the Ocean Decade") aims to reverse this trend through scientific research and technological innovation, providing a solid scientific foundation and practical pathways for the sustainable development of the oceans. This study focuses on the core goals and strategic priorities of the Ocean Decade, exploring in detail how scientific research and technological innovation can help understand and address the causes of ocean health decline, and how these efforts can promote the recovery and protection of marine ecosystems. By analyzing specific case studies, this research demonstrates successful projects implemented globally, such as the establishment of the Global Ocean Observing System and protection projects for key marine ecosystems, and how these projects offer viable solutions for the restoration of ocean health. This study aims to emphasize the central role of scientific research and technological innovation in addressing global ocean challenges and promoting sustainable development of the oceans. It is hoped that the discussions in this study will provide references and insights for future ocean science research and policy-making.

Spatial downscaling of climate change scenarios can be a significant source of uncertainty in simulating climatic impacts on soil erosion, hydrology, and crop production. The objective of this study is to compare responses of simulated soil erosion, surface hydrology, and wheat and maize yields to two (implicit and explicit) spatial downscaling methods used to downscale the A2a, B2a, and GGa1 climate change scenarios projected by the Hadley Centre’s global climate model (HadCM3). The explicit method, in contrast to the implicit method, explicitly considers spatial differences of climate scenarios and variability during downscaling. Monthly projections of precipitation and temperature during 1950–2039 were used in the implicit and explicit spatial downscaling. A stochastic weather generator (CLIGEN) was then used to disaggregate monthly values to daily weather series following the spatial downscaling. The Water Erosion Prediction Project (WEPP) model was run for a wheat–wheat–maize rotation under conventional tillage at the 8.7 and 17.6% slopes in southern Loess Plateau of China. Both explicit and implicit methods projected general increases in annual precipitation and temperature during 2010–2039 at the Changwu station. However, relative climate changes downscaled by the explicit method, as compared to the implicit method, appeared more dynamic or variable. Consequently, the responses to climate change, simulated with the explicit method, seemed more dynamic and sensitive. For a 1% increase in precipitation, percent increases in average annual runoff (soil loss) were 3–6 (4–10) times greater with the explicit method than those with the implicit method. Differences in grain yield were also found between the two methods. These contrasting results between the two methods indicate that spatial downscaling of climate change scenarios can be a significant source of uncertainty, and further underscore the importance of proper spatial treatments of climate change scenarios, and especially climate variability, prior to impact simulation. The implicit method, which applies aggregated climate changes at the GCM grid scale directly to a target station, is more appropriate for simulating a first-order regional response of nature resources to climate change. But for the site-specific impact assessments, especially for entities that are heavily influenced by local conditions such as soil loss and crop yield, the explicit method must be used.