Search for: All records

Abstract This study utilizes Deep Neural Networks (DNN) to improve the K‐Profile Parameterization (KPP) for the vertical mixing effects in the ocean's surface boundary layer turbulence. The deep neural networks were trained using 11‐year turbulence‐resolving solutions, obtained by running a large eddy simulation model for Ocean Station Papa, to predict the turbulence velocity scale coefficient and unresolved shear coefficient in the KPP. The DNN‐augmented KPP schemes (KPP_DNN) have been implemented in the General Ocean Turbulence Model (GOTM). The KPP_DNN is stable for long‐term integration and more efficient than existing variants of KPP schemes with wave effects. Three different KPP_DNN schemes, each differing in their input and output variables, have been developed and trained. The performance of models utilizing the KPP_DNN schemes is compared to those employing traditional deterministic first‐order and second‐moment closure turbulent mixing parameterizations. Solution comparisons indicate that the simulated mixed layer becomes cooler and deeper when wave effects are included in parameterizations, aligning closer with observations. In the KPP framework, the velocity scale of unresolved shear, which is used to calculate ocean surface boundary layer depth, has a greater impact on the simulated mixed layer than the magnitude of diffusivity does. In the KPP_DNN, unresolved shear depends not only on wave forcing, but also on the mixed layer depth and buoyancy forcing. 

Wind, wave, and acoustic observations are used to test a scaling for ambient sound levels in the ocean that is based on wind speed and the degree of surface wave development (at a given wind speed). The focus of this study is acoustic frequencies in the range 1-20 kHz, for which sound is generated by the bubbles injected during surface wave breaking. Traditionally, ambient sound spectra in this frequency range are scaled by wind speed alone. In this study, we investigate a secondary dependence on surface wave development. For any given wind-speed, ambient sound levels are separated into conditions in which waves are 1) actively developing or 2) fully developed. Wave development is quantified using the non-dimensional wave height, a metric commonly used to analyze fetch or duration limitations in wave growth. This simple metric is applicable in both coastal and open ocean environments. Use of the wave development metric to scale sound spectra is first motivated with observations from a brief case study near the island of Jan Mayen (Norwegian Sea), then robustly tested with long time-series observations of winds and waves at Ocean Station Papa (North Pacific Ocean). When waves are actively developing, ambient sound levels are elevated 2-3 dB across the 1-20 kHz frequency range. This result is discussed in the context of sound generation during wave breaking and sound attenuation by persistent bubble layers. 

Abstract In the past decade, two large marine heatwaves (MHWs) formed in the northeast Pacific near Ocean Station Papa (OSP), one of the oldest oceanic time series stations. Physical, biogeochemical, and biological parameters observed at OSP from 2013 to 2020 are used to assess ocean response and potential impacts on marine life from the 2019 northeast Pacific MHW. The 2019 MHW reached peak surface and subsurface temperature anomalies in the summertime and had both coastal, impacting fisheries, and offshore consequences that could potentially affect multiple trophic levels in the Gulf of Alaska. In the Gulf of Alaska, the 2019 MHW was preceded by calm and stratified upper ocean conditions, which preconditioned the enhanced surface warming in late spring and early summer. The MHW coincided with lower dissolved inorganic carbon and higher pH of surface waters relative to the 2013–2020 period. A spike in the summertime chlorophyll followed by a decrease in surface macronutrients suggests increased productivity in the well‐lit stratified upper ocean during summer 2019. More blue whale calls were recorded at OSP in 2019 compared to the prior year. This study shows how the utility of long‐term, continuous oceanographic data sets and analysis with an interdisciplinary lens is necessary to understand the potential impact of MHWs on marine ecosystems. 

Ocean surface radiation measurement best practices have been developed as a first step to support the interoperability of radiation measurements across multiple ocean platforms and between land and ocean networks. This document describes the consensus by a working group of radiation measurement experts from land, ocean, and aircraft communities. The scope was limited to broadband shortwave (solar) and longwave (terrestrial infrared) surface irradiance measurements for quantification of the surface radiation budget. Best practices for spectral measurements for biological purposes like photosynthetically active radiation and ocean color are only mentioned briefly to motivate future interactions between the physical surface flux and biological radiation measurement communities. Topics discussed in these best practices include instrument selection, handling of sensors and installation, data quality monitoring, data processing, and calibration. It is recognized that platform and resource limitations may prohibit incorporating all best practices into all measurements and that spatial coverage is also an important motivator for expanding current networks. Thus, one of the key recommendations is to perform interoperability experiments that can help quantify the uncertainty of different practices and lay the groundwork for a multi-tiered global network with a mix of high-accuracy reference stations and lower-cost platforms and practices that can fill in spatial gaps. 

The NOAA Pacific Marine Environmental Laboratory (PMEL) Ocean Climate Stations (OCS) project provides in situ measurements for quantifying air-sea interactions that couple the ocean and atmosphere. The project maintains two OceanSITES surface moorings in the North Pacific, one at the Kuroshio Extension Observatory in the Northwest Pacific subtropical recirculation gyre and the other at Station Papa in the Northeast Pacific subpolar gyre. OCS mooring time series are used as in situ references for assessing satellite and numerical weather prediction models. A spinoff of the PMEL Tropical Atmosphere Ocean (TAO) project, OCS moorings have acted as “research aggregating devices.” Working with and attracting wide-ranging partners, OCS scientists have collected process-oriented observations of variability on diurnal, synoptic, seasonal, and interannual timescales associated with anthropogenic climate change. Since 2016, they have worked to expand, test, and verify the observing capabilities of uncrewed surface vehicles and to develop observing strategies for integrating these unique, wind-powered observing platforms within the tropical Pacific and global ocean observing system. PMEL OCS has been at the center of the UN Decade of Ocean Sciences for Sustainable Development (2021–2030) effort to develop an Observing Air-Sea Interactions Strategy (OASIS) that links an expanded network of in situ air-sea interaction observations to optimized satellite observations, improved ocean and atmospheric coupling in Earth system models, and ultimately improved ocean information across an array of essential climate variables for decision-makers. This retrospective highlights not only achievements of the PMEL OCS project but also some of its challenges. 

Abstract. Ship-based time series, some now approaching over 3 decades long, are critical climate records that have dramatically improved our ability to characterize natural and anthropogenic drivers of ocean carbon dioxide (CO2) uptake and biogeochemical processes. Advancements in autonomous marine carbon sensors and technologies over the last 2 decades have led to the expansion of observations at fixed time series sites, thereby improving the capability of characterizing sub-seasonal variability in the ocean. Here, we present a data product of 40 individual autonomous moored surface ocean pCO2 (partial pressure of CO2) time series established between 2004 and 2013, 17 also include autonomous pH measurements. These time series characterize a wide range of surface ocean carbonate conditions in different oceanic (17 sites), coastal (13 sites), and coral reef (10 sites) regimes. A time of trend emergence (ToE) methodology applied to the time series that exhibit well-constrained daily to interannual variability and an estimate of decadal variability indicates that the length of sustained observations necessary to detect statistically significant anthropogenic trends varies by marine environment. The ToE estimates for seawater pCO2 and pH range from 8 to 15 years at the open ocean sites, 16 to 41 years at the coastal sites, and 9 to 22 years at the coral reef sites. Only two open ocean pCO2 time series, Woods Hole Oceanographic Institution Hawaii Ocean Time-series Station (WHOTS) in the subtropical North Pacific and Stratus in the South Pacific gyre, have been deployed longer than the estimated trend detection time and, for these, deseasoned monthly means show estimated anthropogenic trends of 1.9±0.3 and 1.6±0.3 µatm yr−1, respectively. In the future, it is possible that updates to this product will allow for the estimation of anthropogenic trends at more sites; however, the product currently provides a valuable tool in an accessible format for evaluating climatology and natural variability of surface ocean carbonate chemistry in a variety of regions. Data are available at https://doi.org/10.7289/V5DB8043 and https://www.nodc.noaa.gov/ocads/oceans/Moorings/ndp097.html (Sutton et al., 2018).