Assessing the skill of a high-resolution marine biophysical model using geostatistical analysis of mesoscale ocean chlorophyll variability from field observations and remote sensing

© The Author(s), 2021. This article is distributed under the terms of the Creaive Commons Attribution License. The definitive version was published in Eveleth, R., Glover, D. M., Long, M. C., Lima, I. D., Chase, A. P., & Doney, S. C. . Assessing the skill of a high-resolution marine biophysical model using geostatistical analysis of mesoscale ocean chlorophyll variability from field observations and remote sensing. Frontiers in Marine Science, 8, (2021): 612764, https://doi.org/10.3389/fmars.2021.612764.High-resolution ocean biophysical models are now routinely being conducted at basin and global-scale, opening opportunities to deepen our understanding of the mechanistic coupling of physical and biological processes at the mesoscale. Prior to using these models to test scientific questions, we need to assess their skill. While progress has been made in validating the mean field, little work has been done to evaluate skill of the simulated mesoscale variability. Here we use geostatistical 2-D variograms to quantify the magnitude and spatial scale of chlorophyll a patchiness in a 1/10th-degree eddy-resolving coupled Community Earth System Model simulation. We compare results from satellite remote sensing and ship underway observations in the North Atlantic Ocean, where there is a large seasonal phytoplankton bloom. The coefficients of variation, i.e., the arithmetic standard deviation divided by the mean, from the two observational data sets are approximately invariant across a large range of mean chlorophyll a values from oligotrophic and winter to subpolar bloom conditions. This relationship between the chlorophyll a mesoscale variability and the mean field appears to reflect an emergent property of marine biophysics, and the high-resolution simulation does poorly in capturing this skill metric, with the model underestimating observed variability under low chlorophyll a conditions such as in the subtropics.This work was supported in part by the National Aeronautics and Space Administration (NASA) as part of the North Atlantic Aerosol and Marine Ecosystems Study (NAAMES; NASA grant 80NSSC18K0018). The CESM project is supported by the National Science Foundation and the Office of Science (BER) of the United States Department of Energy. Computing resources were provided by the Climate Simulation Laboratory at NCAR’s Computational and Information Systems Laboratory (CISL), sponsored by the National Science Foundation and other agencies. This research was enabled by CISL compute and storage resources

Phytoplankton Phenology in the North Atlantic: Insights From Profiling Float Measurements

There was a typographical error in Equation (1) of our article, where the first term in the denominator should be 0.022 rather than 0.22. The fully corrected equation should be: (Formula presented.). The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated

Spring–summer net community production, new production, particle export and related water column biogeochemical processes in the marginal sea ice zone of the Western Antarctic Peninsula 2012–2014

New production (New P, the rate of net primary production (NPP) supported by exogenously supplied limiting nutrients) and net community production (NCP, gross primary production not consumed by community respiration) are closely related but mechanistically distinct processes. They set the carbon balance in the upper ocean and define an upper limit for export from the system. The relationships, relative magnitudes and variability of New P (from 15NO3– uptake), O2 : argon-based NCP and sinking particle export (based on the 238U : 234Th disequilibrium) are increasingly well documented but still not clearly understood. This is especially true in remote regions such as polar marginal ice zones. Here we present a 3-year dataset of simultaneous measurements made at approximately 50 stations along the Western Antarctic Peninsula (WAP) continental shelf in midsummer (January) 2012–2014. Net seasonal-scale changes in water column inventories (0–150 m) of nitrate and iodide were also estimated at the same stations. The average daily rates based on inventory changes exceeded the shorter-term rate measurements. A major uncertainty in the relative magnitude of the inventory estimates is specifying the start of the growing season following sea-ice retreat. New P and NCP(O2) did not differ significantly. New P and NCP(O2) were significantly greater than sinking particle export from thorium-234. We suggest this is a persistent and systematic imbalance and that other processes such as vertical mixing and advection of suspended particles are important export pathways

OOI Biogeochemical Sensor Data: Best Practices and User Guide. Version 1.0.0.

The OOI Biogeochemical Sensor Data Best Practices and User Guide is intended to provide current and prospective users of data generated by biogeochemical sensors deployed on the Ocean Observatories Initiative (OOI) arrays with the information and guidance needed for them to ensure that the data is science-ready. This guide is aimed at researchers with an interest or some experience in ocean biogeochemical processes. We expect that users of this guide will have some background in oceanography, however we do not assume any prior experience working with biogeochemical sensors or their data. While initially envisioned as a “cookbook” for end users seeking to work with OOI biogeochemical (BGC) sensor data, our Working Group and Beta Testers realized that the processing required to meet the specific needs of all end users across a wide range of potential scientific applications and combinations of OOI BGC data from different sensors and platforms couldn’t be synthesized into a single “recipe”. We therefore provide here the background information and principles needed for the end user to successfully identify and understand all the available “ingredients” (data), the types of “cooking” (end user processing) that are recommended to prepare them, and a few sample “recipes” (worked examples) to support end users in developing their own “recipes” consistent with the best practices presented here. This is not intended to be an exhaustive guide to each of these sensors, but rather a synthesis of the key information to support OOI BGC sensor data users in preparing science-ready data products. In instances when more in-depth information might be helpful, references and links have been provided both within each chapter and in the Appendix

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Physical and biological controls on oxygen saturation variability in the upper Arctic Ocean

Abstract Employing continuous in situ measurements of dissolved O 2 /Ar and O 2 in the Arctic Ocean, we investigate the mechanisms controlling the physical (abiotic) and biological oxygen saturation state variability in the surface ocean beneath sea ice. O 2 /Ar measurements were made underway using Equilibrator Inlet Mass Spectrometry (EIMS) during an icebreaker survey transiting the upper Arctic Ocean across the North Pole in late summer 2011. Using concurrently collected measurements of total oxygen, we devolve biological oxygen saturation and physical oxygen (Ar) saturation signals at unprecedented horizontal resolution in the surface ocean. In the Nansen Basin, Ar is undersaturated up to 27% while biological oxygen supersaturation peaks at 18.4%. We attribute this to ice melt, Atlantic Water influence and/or cooling. In the Canadian Basin, Ar is supersaturated up to 3%, likely because of Ar injection from freezing processes and long residence times of gas under ice cover. The overall Canadian Basin to Eurasian Basin gradient of Ar supersaturation to undersaturation may reflect net freezing in the Canadian Basin and net melting in the Eurasian Basin over several seasons, either by Pacific to Atlantic sector ice transport or local changes over time. Ar saturation could thereby provide large-scale high-resolution estimates of current and future changes in these processes. O 2 /Ar supersaturation averages 4.9% with peaks up to 9.8% where first year ice and abundant melt ponds likely allow sufficient light for blooms in ice-covered regions

Can We Estimate Air-Sea Flux of Biological O2 From Total Dissolved Oxygen?

In this study, we compare mechanistic and empirical approaches to reconstructing the air-sea gas flux of biological oxygen ()by parameterizing the physical oxygen saturation anomaly (ΔO2[phy]) in order to separate the biological contribution from total oxygen. The first approach matches ΔO2[phy] to the monthly climatology of the Argon saturation anomaly from a global ocean circulation model’s output. The second approach derives ΔO2[phy] from an iterative mass balance model forced by satellite-based physical drivers of ΔO2[phy] prior to the sampling day by assuming that air-sea interactions are the dominant factors driving the surface ΔO2[phy]. The final approach leverages the machine-learning technique of Genetic Programming (GP) to search for the functional relationship between ΔO2[phy] and biophysicochemical parameters. We compile simultaneous measurements of O2/Ar and O2 concentration from fourteen cruises to train the GP algorithm and test the validity and applicability of our modeled ΔO2[phy] and . Among the approaches, the GP approach, which incorporates ship-based measurements and historical records of physical parameters from the reanalysis products, provides the most robust predictions (R2=0.74 for ΔO2[phy] and 0. 72 for ; RMSE=1.4 % for ΔO2[phy] and 7.3 mmol O2 m-2 d-1 for ). We use the empirical formulation derived from GP approach to reconstruct regional, inter-annual, and decadal variability of based on historical oxygen records. Overall, our study represents a first attempt at deriving from snapshot measurements of oxygen, thereby paving the way toward using historical O2 data and a rapidly growing number of O2 measurements on autonomous platforms for independent insight into the biological pump

Can We Estimate Air-Sea Flux of Biological O-2 From Total Dissolved Oxygen?

In this study, we compare mechanistic and empirical approaches to reconstruct the air-sea flux of biological oxygen (urn:x-wiley:08866236:media:gbc21326:gbc21326-math-0001) by parameterizing the physical oxygen saturation anomaly (ΔO2[phy]) in order to separate the biological contribution from total oxygen. The first approach matches ΔO2[phy] to the monthly climatology of the argon saturation anomaly from a global ocean circulation model\u27s output. The second approach derives ΔO2[phy] from an iterative mass balance model forced by satellite-based physical drivers of ΔO2[phy] prior to the sampling day by assuming that air-sea interactions are the dominant factors driving the surface ΔO2[phy]. The final approach leverages the machine-learning technique of Genetic Programming (GP) to search for the functional relationship between ΔO2[phy] and biophysicochemical parameters. We compile simultaneous measurements of O2/Ar and O2 concentration from 14 cruises to train the GP algorithm and test the validity and applicability of our modeled ΔO2[phy] and urn:x-wiley:08866236:media:gbc21326:gbc21326-math-0002. Among the approaches, the GP approach, which incorporates ship-based measurements and historical records of physical parameters from the reanalysis products, provides the most robust predictions (R2 = 0.74 for ΔO2[phy] and 0.72 for urn:x-wiley:08866236:media:gbc21326:gbc21326-math-0003; RMSE = 1.4% for ΔO2[phy] and 7.1 mmol O2 m−2 d−1 for urn:x-wiley:08866236:media:gbc21326:gbc21326-math-0004). We use the empirical formulation derived from GP approach to reconstruct regional, inter-annual, and decadal variability of urn:x-wiley:08866236:media:gbc21326:gbc21326-math-0005 based on historical oxygen records. Overall, our study represents a first attempt at deriving urn:x-wiley:08866236:media:gbc21326:gbc21326-math-0006 from snapshot measurements of oxygen, thereby paving the way toward using historical O2 data and a rapidly growing number of O2 measurements on autonomous platforms for independent insight into the biological pump

Linking patterns of net community production and marine microbial community structure in the western North Atlantic

Marine net community production (NCP) tracks uptake of carbon by plankton communities and its potential transport to depth. Relationships between marine microbial community composition and NCP currently remain unclear despite their importance for assessing how different taxa impact carbon export. We conducted 16 and 18S rRNA gene (rDNA) sequencing on samples collected across the Western North Atlantic in parallel with high-resolution O-2/Ar-derived NCP measurements. Using an internal standard technique to estimate in-situ prokaryotic and eukaryotic rDNA abundances per liter, we employed statistical approaches to relate patterns of microbial diversity to NCP. Taxonomic abundances calculated using internal standards provided valuable context to traditional relative abundance metrics. A bloom in the Mid-Atlantic Bight featured high eukaryote abundances with low eukaryotic diversity and was associated with the harmful algal bloom-forming Aureococcusanophagefferens, phagotrophic algae, heterotrophic flagellates, and particle-associated bacteria. These results show that coastal Aureococcus blooms host a distinct community associated with regionally significant peaks in NCP. Meanwhile, weak relationships between taxonomy and NCP in less-productive waters suggest that productivity across much of this region is not linked to specific microplankton taxa