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NASA NTRS2018

The NASA Roadmap to Ocean Worlds

In this article, we summarize the work of the NASA Outer Planets Assessment Group (OPAG) Roadmaps to Ocean Worlds (ROW) group. The aim of this group is to assemble the scientific framework that will guide the exploration of ocean worlds, and to identify and prioritize science objectives for ocean worlds over the next several decades. The overarching goal of an Ocean Worlds exploration program as defined by ROW is to “identify ocean worlds, characterize their oceans, evaluate their habitability, search for life, and ultimately understand any life we find.” The ROW team supports the creation of an exploration program that studies the full spectrum of ocean worlds, that is, not just the exploration of known ocean worlds such as Europa but candidate ocean worlds such as Triton as well. The ROW team finds that the confirmed ocean worlds Enceladus, Titan, and Europa are the highest priority bodies to target in the near term to address ROW goals. Triton is the highest priority candidate ocean world to target in the near term. A major finding of this study is that, to map out a coherent Ocean Worlds Program, significant input is required from studies here on Earth; rigorous Research and Analysis studies are called for to enable some future ocean worlds missions to be thoughtfully planned and undertaken. A second finding is that progress needs to be made in the area of collaborations between Earth ocean scientists and extraterrestrial ocean scientists.

Hendrix, Amanda R.↗

NASA NTRS2018

Consequences of Different Air-Sea Feedbacks on Ocean Using MITgcm and MERRA-2 Forcing: Implications for Coupled Data Assimilation Systems

Ocean surface flux estimates from atmospheric and oceanic reanalyses contain errors that compensate for inaccuracies in the respective atmosphere and ocean models used to generate these reanalyses. A conundrum for climate studies is the discrepancy between surface fluxes that minimize model-data differences for an atmosphere-only model vs surface fluxes that minimize model-data differences for an ocean model. As a first step towards a consistent coupled ocean-atmosphere data-assimilation (DA) system, we compare surface net heat flux from a state-of-the-art atmospheric reanalysis, the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), to net heat flux from a state-of-the-art ocean state estimate, the Estimating the Circulation and Climate of the Ocean Version 4 (ECCO-v4). The possible impacts of the MERRA-2 and ECCO-v4 air-sea net heat flux difference in a coupled DA system were assessed using a set of experiments designed to imitate different “flavors” of a coupled DA system in an ocean-only setup. This was done by forcing the ECCO-v4 underlying ocean model - the Massachusetts Institute of Technology general circulation model (MITgcm) - with different sets of MERRA-2 fields and utilizing different forcing methods. By doing so we were able to turn off different air-sea feedbacks which, in a coupled DA setup, are partially muted by the constraining observations. The set of experiments, therefore, represents a range of active feedbacks in different “flavors” of coupled data-assimilation systems. For the period 1992–2011, MERRA-2 net heat flux has a global mean difference of -4.9 Wm(exp -2) relative to ECCO-v4. When MERRA-2 surface fields are used to force MITgcm, imbalances in the energy and the hydrological cycles of MERRA-2, which are directly related to the fact that MERRA-2 was created without an interactive ocean, propagate to the ocean. The experiment in which MITgcm is forced with MERRA-2 fluxes (MERRA-2-flux experiment) results in a 2.5°C global mean Sea Surface Temperature (SST) cooling, a 1m reduction in global mean sea level, and other drastic changes in the large scale ocean circulation relative to those resulting when the MITgcm is forced with the optimized ECCO-v4 net heat flux (the ECCO-v4 experiment itself). When MITgcm is forced with MERRA-2 state variables (MERRA-2-state experiment), the SST is somewhat restored to the observed SST, but the errors are shifted to the water cycle, resulting in a global mean sea level increase of 2.7 m. To further explore the pros and cons of these two approaches, we introduce a new intermediate forcing method in which the ocean is forced with turbulent fluxes but has a long wave feedback. This method, unlike MERRA-2 state, preserves the MERRA-2 water and salinity cycles, and it reduces the SST error compared to the MERRA-2-flux experiment, but the SST is not as good as that in the MERRA-2-state experiment. Our results have implications for ocean-model forcing recipes and clearly reveal the undesirable consequences of limiting the feedbacks in either these types of experiments or in coupled DA.

Ehud Strobach↗

NASA NTRS2023

The Southern Ocean Carbon and Climate Observations and Modeling (SOCCOM) Project: A Review

The Southern Ocean serves as the primary gateway through which the intermediate, deep, and bottom waters of the ocean interact with the surface ocean (and thus the atmosphere), and it has a profound influence on the oceanic uptake of anthropogenic carbon and heat as well as nutrient resupply from the abyss to the surface. Yet it has been the least observed and understood region of the world ocean. The Southern Ocean Carbon and Climate Observations and Modeling (SOCCOM) project was implemented in 2014 with a goal to help remedy this deficit in observations and understanding. The SOCCOM project is based on two major advances that have the potential to transform understanding of the Southern Ocean. The first is the development of new biogeochemical sensors mounted on autonomous profiling floats that allow sampling of ocean biogeochemistry in 3-dimensional space. Floats may detect processes with a temporal resolution that ranges from hours to years. The second is that the climate modeling community finally has the computational resources and physical understanding to develop fully coupled climate models that can represent crucial, mesoscale processes in the Southern Ocean, as well as corresponding models that assimilate observations to produce a state estimate. The observational component, based on deployment of profiling floats with oxygen, nitrate, pH and bio-optical sensors, is generating vast amounts of new biogeochemical data that provide a year-round view of the Southern Ocean from the surface to 2000 m. The modeling effort is applying these observations and enhancing our understanding of the current ocean, and reducing uncertainty in projections of future carbon and nutrient cycles and climate. After nine years of operation, including a project renewal in the sixth year, the SOCCOM project has deployed more than 260 profiling floats. These floats have collected over 27,000 vertical profiles throughout the Southern Ocean. A data-assimilating biogeochemical state estimate model has been implemented. Here, the design of the SOCCOM project is reviewed and the scientific results that have been obtained are described. The project’s capability to help meet the observing system priorities outlined for a notional UN Decade for Ocean Sciences Southern Ocean observing system is assessed.

Jorge L. Sarmiento↗

DOE OSTI2021

Assessing the sequestration time scales of some ocean-based carbon dioxide reduction strategies

Abstract Ocean-based carbon dioxide (CO 2 ) removal (CDR) strategies are an important part of the portfolio of approaches needed to achieve negative greenhouse gas emissions. Many ocean-based CDR strategies rely on injecting CO 2 or organic carbon (that will eventually become CO 2 ) into the ocean interior, or enhancing the ocean’s biological pump. These approaches will not result in permanent sequestration, because ocean currents will eventually return the injected CO 2 back to the surface, where it will be brought into equilibrium with the atmosphere. Here, a model of steady state global ocean circulation and mixing is used to assess the time scales over which CO 2 injected in the ocean interior remains sequestered from the atmosphere. There will be a distribution of sequestration times for any single discharge location due to the infinite number of pathways connecting a location at depth with the sea surface. The resulting probability distribution is highly skewed with a long tail of very long transit times, making mean sequestration times much longer than typical time scales. Deeper discharge locations will sequester purposefully injected CO 2 much longer than shallower ones and median sequestration times are typically decades to centuries, and approach 1000 years in the deep North Pacific. Large differences in sequestration times occur both within and between the major ocean basins, with the Pacific and Indian basins generally having longer sequestration times than the Atlantic and Southern Oceans. Assessments made over a 50 year time horizon illustrates that most of the injected carbon will be retained for injection depths greater than 1000 m, with several geographic exceptions such as the Western North Atlantic. Ocean CDR strategies that increase upper ocean ecosystem productivity with the goal of exporting more carbon to depth will have mainly a short-term influence on atmospheric CO 2 levels because ∼70% will be transported back to the surface ocean within 50 years. The results presented here will help plan appropriate ocean CDR strategies that can help limit climate damage caused by fossil fuel CO 2 emissions.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Reduced viscosity steadily weakens oceanic currents

The viscosity of both air and water is temperature dependent. A rising temperature leads to an increased viscosity for air but a decreased viscosity for water. As climate becomes warmer, this increased air viscosity can partly inhibit the reduction of wind stress over the ocean, and the reduced water viscosity causes less downward momentum and heat transport. As these opposing effects of warming on air and water viscosity are not included in the state-of-the-art climate models, the understanding of their potential impacts on the response of the climate system to the anthropogenic warming is lacking. Here, via analyzing the Simple Ocean Data Assimilation oceanic reanalysis dataset, we show that the ocean heat content increases at a rate of ~1.3 × 10 22 J/yr over 35 years, which leads to a continuous reduction of oceanic viscosity. As a result, the ocean vertical shear enhances with a shoaling of the mixed layer depth and a reduced vertical linkage in the ocean. Our calculations show a reduction of the oceanic kinetic energy at a rate of ~2.4 × 10 16 J/yr. Potentially, this could generate far-reaching impacts on the energy storage of the climate system and, hence, could pace the global warming. Thus, it is important to include the temperature-dependent viscosity in our climate models. Freshwater discharged from polar ice sheets and mountain glaciers also contributes to the reduction in oceanic viscosity but, at present, to a lesser extent than that in oceanic warming. Reduced oceanic viscosity, therefore, is an important, but hitherto overlooked, response to a warming climate and contributes to many recent weather extremes including heavier rainfall rates in hurricanes, slackening of the polar vortex, and oceanic heat waves.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2019

Going Beyond Standard Ocean Color Observations: Lidar and Polarimetry

Passive ocean color images have provided a sustained synoptic view of the distribution of ocean optical properties and color and biogeochemical parameters for the past 20-plus years. These images have revolutionized our view of the ocean. Remote sensing of ocean color has relied on measurements of the radiance emerging at the top of the atmosphere, thus neglecting the polarization and the vertical components. Ocean color remote sensing utilizes the intensity and spectral variation of visible light scattered upward from beneath the ocean surface to derive concentrations of biogeochemical constituents and inherent optical properties within the ocean surface layer. However, these measurements have some limitations. Specifically, the measured property is a weighted-integrated value over a relatively shallow depth, it provides no information during the night and retrievals are compromised by clouds, absorbing aerosols, and low Sun zenithal angles. In addition, ocean color data provide limited information on the morphology and size distribution of marine particles. Major advances in our understanding of global ocean ecosystems will require measurements from new technologies, specifically lidar and polarimetry. These new techniques have been widely used for atmospheric applications but have not had as much as interest from the ocean color community. This is due to many factors including limited access to in-situ instruments and/or space-borne sensors and lack of attention in university courses and ocean science summer schools curricula. However, lidar and polarimetry technology will complement standard ocean color products by providing depth-resolved values of attenuation and scattering parameters and additional information about particle morphology and chemical composition. This review aims at presenting the basics of these techniques, examples of applications and at advocating for the development of in-situ and space-borne sensors. Recommendations are provided on actions that would foster the embrace of lidar and polarimetry as powerful remote sensing tools by the ocean science community.

Jamet, Cedric↗

NASA NTRS2023

Ocean Carbon From Space: Current Status and Priorities for the Next Decade

The ocean plays a central role in modulating the Earth’s carbon cycle. Monitoring how the ocean carbon cycle is changing is fundamental to managing climate change. Satellite remote sensing is currently our best tool for viewing the ocean surface globally and systematically, at high spatial and temporal resolutions, and the past few decades have seen an exponential growth in studies utilising satellite data for ocean carbon research. Satellite-based observations must be combined with in-situ observations and models, to obtain a comprehensive view of ocean carbon pools and fluxes. To help prioritise future research in this area, a workshop was organised that assembled leading experts working on the topic, from around the world, including remote-sensing scientists, field scientists and modellers, with the goal to articulate a collective view of the current status of ocean carbon research, identify gaps in knowledge, and formulate a scientific roadmap for the next decade, with an emphasis on evaluating where satellite remote sensing may contribute. A total of 449 scientists and stakeholders participated (with balanced gender representation), from North and South America, Europe, Asia, Africa, and Oceania. Sessions targeted both inorganic and organic pools of carbon in the ocean, in both dissolved and particulate form, as well as major fluxes of carbon between reservoirs (e.g., primary production) and at interfaces (e.g., air-sea and land–ocean). Extreme events, blue carbon and carbon budgeting were also key topics discussed. Emerging priorities identified include: expanding the networks and quality of in-situ observations; improved satellite retrievals; improved uncertainty quantification; improved understanding of vertical distributions; integration with models; improved techniques to bridge spatial and temporal scales of the different data sources; and improved fundamental understanding of the ocean carbon cycle, and of the interactions among pools of carbon and light. We also report on priorities for the specific pools and fluxes studied, and highlight issues and concerns that arose during discussions, such as the need to consider the environmental impact of satellites or space activities; the role satellites can play in monitoring ocean carbon dioxide removal approaches; economic valuation of the satellite based information; to consider how satellites can contribute to monitoring cycles of other important climatically-relevant compounds and elements; to promote diversity and inclusivity in ocean carbon research; to bring together communities working on different aspects of planetary carbon; maximising use of international bodies; to follow an open science approach; to explore new and innovative ways to remotely monitor ocean carbon; and to harness quantum computing. Overall, this paper provides a comprehensive scientific roadmap for the next decade on how satellite remote sensing could help monitor the ocean carbon cycle, and its links to the other domains, such as terrestrial and atmosphere.

oceanography↗

DOE OSTI2023

Simulations of ocean deoxygenation in the historical era: insights from forced and coupled models

Ocean deoxygenation due to anthropogenic warming represents a major threat to marine ecosystems and fisheries. Challenges remain in simulating the modern observed changes in the dissolved oxygen (O 2 ). Here, we present an analysis of upper ocean (0-700m) deoxygenation in recent decades from a suite of the Coupled Model Intercomparison Project phase 6 (CMIP6) ocean biogeochemical simulations. The physics and biogeochemical simulations include both ocean-only (the Ocean Model Intercomparison Project Phase 1 and 2, OMIP1 and OMIP2) and coupled Earth system (CMIP6 Historical) configurations. We examine simulated changes in the O 2 inventory and ocean heat content (OHC) over the past 5 decades across models. The models simulate spatially divergent evolution of O 2 trends over the past 5 decades. The trend (multi-model mean and spread) for upper ocean global O 2 inventory for each of the MIP simulations over the past 5 decades is 0.03 ± 0.39×10 14 [mol/decade] for OMIP1, -0.37 ± 0.15×10 14 [mol/decade] for OMIP2, and -1.06 ± 0.68×10 14 [mol/decade] for CMIP6 Historical, respectively. The trend in the upper ocean global O2 inventory for the latest observations based on the World Ocean Database 2018 is -0.98×10 14 [mol/decade], in line with the CMIP6 Historical multi-model mean, though this recent observations-based trend estimate is weaker than previously reported trends. A comparison across ocean-only simulations from OMIP1 and OMIP2 suggests that differences in atmospheric forcing such as surface wind explain the simulated divergence across configurations in O 2 inventory changes. Additionally, a comparison of coupled model simulations from the CMIP6 Historical configuration indicates that differences in background mean states due to differences in spin-up duration and equilibrium states result in substantial differences in the climate change response of O 2 . Finally, we discuss gaps and uncertainties in both ocean biogeochemical simulations and observations and explore possible future coordinated ocean biogeochemistry simulations to fill in gaps and unravel the mechanisms controlling the O 2 changes.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRSDate not supplied

Visioning a Global Carbon Monitoring System That Can Quantify and Attribute Ocean Carbon Dioxide Removal: Where Are We Now and How Might We Get There?

As atmospheric greenhouse gas concentrations and the resulting social costs rise, it is increasingly important to conduct research on technical approaches to remove carbon dioxide from the atmosphere at the gigaton scale. One highly uncertain but perhaps plausible solution is ocean carbon dioxide (CO2) removal (CDR), which encompasses a suite of proposed techniques for increasing the net flux of CO2 from the atmosphere to the ocean. As reflected in a 2022 National Academy of Sciences report, interest in ocean CDR is rapidly growing, and new stakeholders from a wide range of fields want access to air-sea CO2 flux and ocean carbon information to understand the potential risks and benefits of ocean CDR. Moreover, there is an urgent need for research to develop a scientific basis to support these societal needs. At the same time, there is a growing need to vision a global carbon monitoring system that can quantify natural and anthropogenic perturbations in the air-sea CO2 flux to quantify and attribute the impacts of ocean CDR efforts on the ocean carbon sink. Here, we propose to present nascent efforts at the Ames Research Center to leverage NASA's unique remote sensing and computational capabilities and expertise in basic and applied Earth science to meet the needs of the ocean CDR stakeholder community as represented by the OceanVisions network. Specifically we aim to develop a global ocean CDR scenario explorer with two objectives: 1) to make quantitative estimates of the air-sea CO2 flux and its uncertainty more accessible to a wide range of users, and 2) to enable these diverse users to interactively explore the uncertain impacts on air-sea CO2 fluxes in a wide range of ocean CDR scenarios. The information would be provided to users via a public graphical user interface, and the results would be based on a new synthesis of scientific observations and knowledge in a global ocean mixed layer inverse model run on supercomputers. The proposed tool aims to fill a unique and valuable niche for users and the scientific community by balancing tradeoffs between user interests, scientific knowledge, and technical capabilities. We will conclude by visioning the potential implications for a global carbon monitoring system in a world with gigaton scale ocean CDR.

Global↗

NASA NTRS2022

Using Machine Learning for Timely Estimates of Ocean Color Information From Hyperspectral Satellite Measurements in the Presence of Clouds, Aerosols, and Sunglint

Retrievals of ocean color from space are important for better understanding of the ocean ecosystem but can be limited under conditions such as clouds, aerosols, and sunglint. Many ocean color algorithms use a few selected spectral bands to perform an atmospheric correction and then derive the upwelling radiance from the ocean. The limitations in the atmospheric correction under certain conditions lead to many gaps in daily spatial coverage of ocean color retrievals. To address these limitations, we introduce a new approach that uses machine learning to estimate ocean color from top of atmosphere radiances or reflectance measurements. In this approach, a principal component analysis is used to decompose the hyperspectral measurements into spectral features that describe the scattering and absorption of the atmosphere and the underlying surface. The coefficients of the principal components are then used to train a neural network to predict ocean color properties derived from the MODIS atmospheric correction algorithm. This machine learning approach is independent of a priori information and does not rely on any radiative transfer modeling. We apply the approach to two hyperspectral UV/VIS instruments, the ozone monitoring instrument (OMI) and the TROPOspheric Monitoring Instrument (TROPOMI), using measurements from 320–500 nm to show that it can be used to reproduce ocean color properties in less-than-ideal conditions. This machine learning approach complements the current atmospheric correction ocean color retrievals by filling in the gaps resulting from cloud, aerosol, and sunglint contamination. This method can be applied to the future hyperspectral Ocean Color Instrument (OCI), which will be onboard NASA’s Plankton, Aerosol Cloud, ocean Ecosystem (PACE) ocean color satellite set to launch in 2024.

Ocean color↗

DOE OSTI2025

Acidification in the Southern Ocean – current state and future challenges

The Southern Ocean is especially sensitive to ocean acidification, a process where carbon dioxide in the atmosphere is absorbed by the ocean, thereby changing its acidity levels. Observations show that open ocean waters across the Southern Ocean experienced faster rates of acidification over the past four decades than non-polar waters. Model simulations project more severe ocean acidification in Antarctic coastal waters than in the open ocean. Ocean acidification negatively impacts many Southern Ocean species, but our understanding of whole-ecosystem impacts is incomplete. Furthermore, it is important that we both maintain and expand observational capabilities and mechanistic experiments in this rapidly changing and vulnerable region of the ocean to understand the impacts of ocean acidification on Southern Ocean ecosystems.

Nissen, Cara [University of Amsterdam (The Netherl↗

NASA NTRS1992

The Global Ocean Observing System

A Global Ocean Observing System (GOOS) should be established now with international coordination (1) to address issues of global change, (2) to implement operational ENSO forecasts, (3) to provide the data required to apply global ocean circulation models, and (4) to extract the greatest value from the one billion dollar investment over the next ten years in ocean remote sensing by the world's space agencies. The objectives of GOOS will focus on climatic and oceanic predictions, on assessing coastal pollution, and in determining the sustainability of living marine resources and ecosystems. GOOS will be a complete system including satellite observations, in situ observations, numerical modeling of ocean processes, and data exchange and management. A series of practical and economic benefits will be derived from the information generated by GOOS. In addition to the marine science community, these benefits will be realized by the energy industries of the world, and by the world's fisheries. The basic oceanic variables that are required to meet the oceanic and predictability objectives of GOOS include wind velocity over the ocean, sea surface temperature and salinity, oceanic profiles of temperature and salinity, surface current, sea level, the extent and thickness of sea ice, the partial pressure of CO2 in surface waters, and the chlorophyll concentration of surface waters. Ocean circulation models and coupled ocean-atmosphere models can be used to evaluate observing system design, to assimilate diverse data sets from in situ and remotely sensed observations, and ultimately to predict future states of the system. The volume of ocean data will increase enormously over the next decade as new satellite systems are launched and as complementary in situ measuring systems are deployed. These data must be transmitted, quality controlled, exchanged, analyzed, and archived with the best state-of-the-art computational methods.

Kester, Dana↗

NASA NTRS1992

Atmospheric correction of AVIRIS data in ocean waters

Hyperspectral data offers unique capabilities for characterizing the ocean environment. The spectral characterization of the composition of ocean waters can be organized into biological and terrigenous components. Biological photosynthetic pigments in ocean waters have unique spectral ocean color signatures which can be associated with different biological species. Additionally, suspended sediment has different scattering coefficients which result in ocean color signatures. Measuring the spatial distributions of these components in the maritime environments provides important tools for understanding and monitoring the ocean environment. These tools have significant applications in pollution, carbon cycle, current and water mass detection, location of fronts and eddies, sewage discharge and fate etc. Ocean color was used from satellite for describing the spatial variability of chlorophyll, water clarity (K(sub 490)), suspended sediment concentration, currents etc. Additionally, with improved atmospheric correction methods, ocean color results produced global products of spectral water leaving radiance (L(sub W)). Ocean color results clearly indicated strong applications for characterizing the spatial and temporal variability of bio-optical oceanography. These studies were largely the results of advanced atmospheric correction techniques applied to multispectral imagery. The atmosphere contributes approximately 80 percent - 90 percent of the satellite received radiance in the blue-green portion of the spectrum. In deep ocean waters, maximum transmission of visible radiance is achieved at 490nm. Conversely, nearly all of the light is absorbed by the water at wavelengths greater than about 650nm and thus appears black. These spectral ocean properties are exploited by algorithms developed for the atmospheric correction used in satellite ocean color processing. The objective was to apply atmospheric correction techniques that were used for procesing satellite Coastal Zone Color Scanner (CZCS) data to AVIRIS data. Quantitative measures of L(sub W) from AVIRIS are compared with ship ground truth data and input into bio-optical models.

Terrie, Gregory↗

NASA NTRS1997

Oceanic Observations

For many years, merchant ships and the naval fleets of various countries have been the major source of data over and in the open ocean. Oceanographic research experiments and process studies in the field have also contributed to the climatological data bases for the global ocean, but, for the most part, these have been limited in duration and extent. However, over the last 10 years under the auspices of the World Climate Research Program and the International Geosphere Biosphere Program the role of the oceans in global and climate change has taken on increased significance. This has created a need for a considerably improved understanding of the seasonal, interannual, decadal and longer time-scale variability of the physical and biogeochemical attributes of the global ocean. As a result, over the past 10 years several major international field programs have been implemented and have had a tremendous impact on the number of in situ observations obtained for the global ocean. The Tropical Ocean Global Atmosphere (TOGA) program, the World Ocean Circulation Experiment (WOCE), and the Joint Global Ocean Flux Study (JGOFS) were designed with observational, modelling, and process study components aimed at analyzing different aspects of the ocean's role in the coupled climate system. In parallel with the field programs, continuous space-based observations of sea surface temperature, sea surface topography, and sea surface winds spanning nearly a decade or longer have become a reality. During this same time period, numerical ocean models and computational power have advanced to the point where the oceanographic observations, both in situ and remotely sensed, can be assimilated into numerical ocean models in order to provide a four-dimensional (x-y-z-t) depiction of the evolving state of the global ocean.

Busalacchi, Antonio J.↗

NASA NTRS2001

Merging Ocean Color Data from Multiple Missions

Oceanic phytoplankton may play an important role in the cycling of carbon on the Earth, through the uptake of carbon dioxide in the process of photosynthesis. Although they are ubiquitous in the global oceans, their abundances and dynamics are difficult to estimate, primarily due to the vast spatial extent of the oceans and the short time scales over which their abundances can change. Consequently, the effects of oceanic phytoplankton on biogeochemical cycling, climate change, and fisheries are not well known. In response to the potential importance of phytoplankton in the global carbon cycle and the lack of comprehensive data, the National Aeronautics and Space Administration (NASA) and the international community have established high priority satellite missions designed to acquire and produce high quality ocean color data. Seven of the missions are routine global observational missions: the Ocean Color and Temperature Sensor (OCTS), the Polarization and Directionality of the Earth's Reflectances sensor (POLDER), Sea-viewing Wide Field-of-view Sensor (SeaWiFS), Moderate Resolution Imaging Spectrometer-AM (MODIS-AM), Medium Resolution Imaging Spectrometer (MERIS), Global Imager (GLI), and MODIS-PM. In addition, there are several other missions capable of providing ocean color data on smaller scales. Most of these missions contain the spectral band complement considered necessary to derive oceanic pigment concentrations (i.e., phytoplankton abundance) and other related parameters. Many contain additional bands that can provide important ancillary information about the optical and biological state of the oceans. Any individual ocean color mission is limited in ocean coverage due to sun glint and clouds. For example, one of the first proposed missions, the SeaWiFS, can provide about 45% coverage of the global ocean in four days and only about 15% in one day.

Gregg, Watson W.↗

NASA NTRS2019

Predictability of Ocean Heat Content From Electrical Conductance

Ocean heat content (OHC) is a key climate variable that needs to be monitored to know how Earth's energy imbalance is changing, yet observing OHC remains a challenge. The present study examines whether a depth integral of the ocean's electrical conductivity (“conductance”), which may be inferred from both in situ methods and satellite magnetometers over the global ocean, could help monitor OHC. The ocean's electrical conductivity locally depends on temperature, salinity, and pressure, but it is not as well known how the conductance depends on OHC and ocean salt content. By examining the output of an ocean state estimate shown to agree well with observations that have not been assimilated, this study evaluates the fundamental limitations of using perfectly known ocean conductance to predict OHC, rather than the challenges associated with accounting for observational error. It is found that the ocean's conductance and OHC fields are nonlinearly related but nevertheless highly correlated. A statistical framework tends to predict OHC more accurately than ocean salt content from ocean conductance in regions where conductivity is more sensitive to salinity than temperature. The annually (bidecadally) averaged OHC can be predicted from a combination of conductance and depth‐averaged conductivity ocean fields to within nearly 0.1% (1%) error globally and even more accurately in many poorly observed (e.g., ice‐covered) regions. Practical application of this statistical framework to monitor OHC requires examination of the effect of uncertainties in the observed bathymetry and ocean conductance, which vary with application.

Trossman, D. S.↗

NASA NTRSDate not supplied

An Intercomparison of High Spectral Resolution Lidar and Satellite Ocean Color Backscatter Retrievals

We present an intercomparison of satellite ocean color and high spectral resolution lidar (HSRL) retrievals of upper ocean particulate backscattering (bbp) and diffuse attenuation coefficients (Kd). Ocean retrievals of bbp and Kd have been performed over a wide variety of optical and ecological domains using the NASA Langley Research Center HSRL-1 (532 nm) and the recently upgraded HSRL-2 (532 & 355 nm). These datasets provide a unique opportunity to perform a critical assessment of the HSRL technique and to explore the utility of HSRL measurements for evaluating the quality of ocean color remote sensing retrievals on regional scales. A matchup dataset of co-located and high-quality HSRL and ocean color remote sensing measurements was created using data from research flights conducted over the western North Atlantic Ocean during the NASA sponsored SABOR, NAAMES, and ACTIVATE campaigns. Overall, comparisons showed a strong agreement between HSRL and ocean color retrievals of bbp and Kd, providing confidence in our ability to retrieve upper ocean optical properties using oceanographic lidar. However, comparisons from individual flights can exhibit deviations that were not attributed to shifts in water column optical domains. In one of these cases, the simultaneous HSRL atmosphere-ocean retrievals from two consecutive days and within the same region were used to identify ocean color atmospheric correction errors resulting from the presence of absorbing aerosols. These results highlight the utility of airborne HSRL as a standalone ocean observing technology and as an independent and calibrated technique for assessing the quality of ocean color remote sensing retrievals.

Brian Collister↗

DOE OSTI2022

Characterizing vertical upper ocean temperature structures in the European Arctic through unsupervised machine learning

In-situ observations of subsurface ocean temperatures are, in many regions, inconsistently distributed in time and space. These spatio-temporal inconsistencies in the observational network lead to difficulties in utilizing those observations effectively for ocean model evaluation or understanding larger-scale ocean characteristics. Model accuracy of subsurface ocean characteristics is especially important within regions that contain complex ocean structures. One such region is the European Arctic which not only contains several types of water masses with unique characteristics, but also wintertime sea ice coverage and complex bathymetry. This study presents an unsupervised neural networking technique that can be used in combination with traditional ocean model evaluation techniques to provide additional information on the accuracy of modeled vertical ocean temperature profiles. Self-organizing maps is an unsupervised machine learning technique that we apply to approximately twenty thousand Argo and CTD temperature profiles from 2012 to 2020 in the European Arctic to categorize the observed vertical ocean temperature structures in the top 150 m. The observed ocean profile categories, or neurons, defined by the self-organizing map show strong spatial and temporal dependencies. We then use the neuron weights, or the learned temperature profile structure of each neuron, to validate the spatial and temporal variability of modeled vertical temperature structures. This analysis gives us new insights about the model’s capabilities to reproduce specific vertical structures of the top-most ocean layer within different regions and seasons. Mapping modeled ocean temperature profiles onto the neuron-space of the observationally-defined self organized map highlights the potential of this method to advance our understanding of model deficiencies in that region.

54 ENVIRONMENTAL SCIENCES↗