Salinity and temperature balances at the SPURS central mooring during fall and winter

Author Posting. © The Oceanography Society, 2015. This article is posted here by permission of The Oceanography Society for personal use, not for redistribution. The definitive version was published in Oceanography 28, no. 1 (2015): 56-65, doi:10.5670/oceanog.2015.06.One part of the Salinity Processes in the Upper-ocean Regional Study (SPURS) field campaign focused on understanding the physical processes affecting the evolution of upper-ocean salinity in the region of climatological maximum sea surface salinity in the subtropical North Atlantic (SPURS-1). An upper-ocean salinity budget provides a useful framework for increasing this understanding. The SPURS-1 program included a central heavily instrumented mooring for making accurate measurements of air-sea surface fluxes, as well as other moorings, Argo floats, and gliders that together formed a dense observational array. Data from this array are used to estimate terms in the upper-ocean salinity and heat budgets during the SPURS-1 campaign, with a focus on the first several months (October 2012 to February 2013) when the surface mixed layer was becoming deeper, fresher, and cooler. Specifically, we examine the salinity and temperature balances for an upper-ocean mixed layer, defined as the layer where the density is within 0.4 kg m–3 of its surface value. The gross features of the evolution of upper-ocean salinity and temperature during this fall/winter season are explained by a combination of evaporation and precipitation at the sea surface, horizontal transport of heat and salt by mixed-layer currents, and vertical entrainment of fresher, cooler fluid into the layer as it deepened. While all of these processes were important in the observed seasonal (fall) freshening at this location in the salinity-maximum region, the variability of salinity on monthly-to-intraseasonal time scales resulted primarily from horizontal advection.J.T. Farrar, A.J. Plueddemann, J.B. Edson, and the deployment of the central mooring were supported by NASA grant NNX11AE84G. L. Rainville, C. Lee, C. Eriksen, and the Seaglider program were supported by NASA grant NNX11AE78G. R. Schmitt was supported by NSF grant OCE-1129646. B. Hodges and D. Fratantoni were supported by NASA grant NNX11AE82G. The Prawler moorings were funded by PMEL. The data analysis was also supported by NASA grant NNX14AH38G

Seasonal prediction of bottom temperature on the Northeast U.S. Continental Shelf

© The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Chen, Z., Kwon, Y.-O., Chen, K., Fratantoni, P., Gawarkiewicz, G., Joyce, T. M., Miller, T. J., Nye, J. A., Saba, V. S., & Stock, B. C. Seasonal prediction of bottom temperature on the Northeast U.S. Continental Shelf. Journal of Geophysical Research: Oceans, 126(5), (2021): e2021JC017187, https://doi.org/10.1029/2021JC017187.The Northeast U.S. shelf (NES) is an oceanographically dynamic marine ecosystem and supports some of the most valuable demersal fisheries in the world. A reliable prediction of NES environmental variables, particularly ocean bottom temperature, could lead to a significant improvement in demersal fisheries management. However, the current generation of climate model-based seasonal-to-interannual predictions exhibits limited prediction skill in this continental shelf environment. Here, we have developed a hierarchy of statistical seasonal predictions for NES bottom temperatures using an eddy-resolving ocean reanalysis data set. A simple, damped local persistence prediction model produces significant skill for lead times up to ∼5 months in the Mid-Atlantic Bight and up to ∼10 months in the Gulf of Maine, although the prediction skill varies notably by season. Considering temperature from a nearby or upstream (i.e., more poleward) region as an additional predictor generally improves prediction skill, presumably as a result of advective processes. Large-scale atmospheric and oceanic indices, such as Gulf Stream path indices (GSIs) and the North Atlantic Oscillation Index, are also tested as predictors for NES bottom temperatures. Only the GSI constructed from temperature observed at 200 m depth significantly improves the prediction skill relative to local persistence. However, the prediction skill from this GSI is not larger than that gained using models incorporating nearby or upstream shelf/slope temperatures. Based on these results, a simplified statistical model has been developed, which can be tailored to fisheries management for the NES.This work was supported by NOAA's Climate Program Office's Modeling, Analysis, Predictions, and Projections (MAPP) Program (NA17OAR4310111, NA19OAR4320074), and Climate Program Office's Climate Variability and Predictability (CVP) Program (NA20OAR4310482). We acknowledge our participation in MAPP's Marine Prediction Task Force

Autonomous multi-platform observations during the Salinity Processes in the Upper-ocean Regional Study

Author Posting. © The Oceanography Society, 2017. This article is posted here by permission of The Oceanography Society for personal use, not for redistribution. The definitive version was published in Oceanography 30, no. 2 (2017): 38–48, doi:10.5670/oceanog.2017.218.The Salinity Processes in the Upper-ocean Regional Study (SPURS) aims to understand the patterns and variability of sea surface salinity. In order to capture the wide range of spatial and temporal scales associated with processes controlling salinity in the upper ocean, research vessels delivered autonomous instruments to remote sites, one in the North Atlantic and one in the Eastern Pacific. Instruments sampled for one complete annual cycle at each of these two sites, which are subject to contrasting atmospheric forcing. The SPURS field programs coordinated sampling from many different platforms, using a mix of Lagrangian and Eulerian approaches. This article discusses the motivations, implementation, and first results of the SPURS-1 and SPURS-2 programs.SPURS is supported by multiple NASA grants, with important additional contributions from the US National Science Foundation, NOAA, and the Office of Naval Research, as well as international agencies. SVP drifters are deployed with support from NASA and the NOAA funded Global Drifter Program at the Lagrangian Drifter Laboratory of the Scripps Institution of Oceanography. SVP-S2 drifters are provided by NOAA-AOML and NASA. PRAWLER mooring development is supported by NOAA’s Office of Oceanic and Atmospheric Research, Ocean Observing and Monitoring Division, and by NOAA/PMEL

Remote climate forcing of decadal-scale regime shifts in Northwest Atlantic shelf ecosystems

Author Posting. © Association for the Sciences of Limnology and Oceanography, 2013. This article is posted here by permission of Association for the Sciences of Limnology and Oceanography for personal use, not for redistribution. The definitive version was published in Association for the Sciences of Limnology and Oceanography, doi:10.4319/lo.2013.58.3.0803.Decadal-scale regime shifts in Northwest Atlantic shelf ecosystems can be remotely forced by climate-associated atmosphere–ocean interactions in the North Atlantic and Arctic Ocean Basins. This remote climate forcing is mediated primarily by basin- and hemispheric-scale changes in ocean circulation. We review and synthesize results from process-oriented field studies and retrospective analyses of time-series data to document the linkages between climate, ocean circulation, and ecosystem dynamics. Bottom-up forcing associated with climate plays a prominent role in the dynamics of these ecosystems, comparable in importance to that of top-down forcing associated with commercial fishing. A broad perspective, one encompassing the effects of basin- and hemispheric-scale climate processes on marine ecosystems, will be critical to the sustainable management of marine living resources in the Northwest Atlantic.Funding for this research was provided by the National Science Foundation as part of the Regional and Pan-Regional Synthesis Phases of the U.S. Global Ocean Ecosystem (GLOBEC) Program

Estimating the predictability of an oceanic time series using linear and nonlinear methods

Author Posting. © American Geophysical Union, 2004. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research 109 (2004): C08002, doi:10.1029/2003JC002148.This study establishes a series of tests to examine the relative utility of nonlinear time series analysis for oceanic data. The performance of linear autoregressive models and nonlinear delay coordinate embedding methods are compared for three numerical and two observational data sets. The two observational data sets are (1) an hourly near-bottom pressure time series from the South Atlantic Bight and (2) an hourly current-meter time series from the Middle Atlantic Bight (MAB). The nonlinear methods give significantly better predictions than the linear methods when the underlying dynamics have low dimensionality. When the dimensionality is high, the utility of nonlinear methods is limited by the length and quality of the time series. On the application side we mainly focus on the MAB data set. We find that the slope velocities are much less predictable than shelf velocities. Predictability on the slope after several hours is no better than the statistical mean. On the other hand, significant predictability of shelf velocities can be obtained for up to at least 12 hours.This research was supported by Office of Naval Research grants N00014-01-1-0260, N00014-92-J-1481, and N10014-99-1-0258

Recent Arctic climate change and its remote forcing of Northwest Atlantic shelf ecosystems

Author Posting. © The Oceanography Society, 2012. This article is posted here by permission of The Oceanography Society for personal use, not for redistribution. The definitive version was published in Oceanography 25, no. 3 (2012): 208-213, doi:10.5670/oceanog.2012.64.During recent decades, historically unprecedented changes have been observed in the Arctic as climate warming has increased precipitation, river discharge, and glacial as well as sea-ice melting. Additionally, shifts in the Arctic's atmospheric pressure field have altered surface winds, ocean circulation, and freshwater storage in the Beaufort Gyre. These processes have resulted in variable patterns of freshwater export from the Arctic Ocean, including the emergence of great salinity anomalies propagating throughout the North Atlantic. Here, we link these variable patterns of freshwater export from the Arctic Ocean to the regime shifts observed in Northwest Atlantic shelf ecosystems. Specifically, we hypothesize that the corresponding salinity anomalies, both negative and positive, alter the timing and extent of water-column stratification, thereby impacting the production and seasonal cycles of phytoplankton, zooplankton, and higher-trophic-level consumers. Should this hypothesis hold up to critical evaluation, it has the potential to fundamentally alter our current understanding of the processes forcing the dynamics of Northwest Atlantic shelf ecosystems.Funding for this research was provided by the National Science Foundation as part of the Regional and Pan-Regional Synthesis Phases of the US Global Ocean Ecosystem (GLOBEC) Program

Spectral counting assessment of protein dynamic range in cerebrospinal fluid following depletion with plasma-designed immunoaffinity columns

<p>Abstract</p> <p>Background</p> <p>In cerebrospinal fluid (CSF), which is a rich source of biomarkers for neurological diseases, identification of biomarkers requires methods that allow reproducible detection of low abundance proteins. It is therefore crucial to decrease dynamic range and improve assessment of protein abundance.</p> <p>Results</p> <p>We applied LC-MS/MS to compare the performance of two CSF enrichment techniques that immunodeplete either albumin alone (IgYHSA) or 14 high-abundance proteins (IgY14). In order to estimate dynamic range of proteins identified, we measured protein abundance with APEX spectral counting method.</p> <p>Both immunodepletion methods improved the number of low-abundance proteins detected (3-fold for IgYHSA, 4-fold for IgY14). The 10 most abundant proteins following immunodepletion accounted for 41% (IgY14) and 46% (IgYHSA) of CSF protein content, whereas they accounted for 64% in non-depleted samples, thus demonstrating significant enrichment of low-abundance proteins. Defined proteomics experiment metrics showed overall good reproducibility of the two immunodepletion methods and MS analysis. Moreover, offline peptide fractionation in IgYHSA sample allowed a 4-fold increase of proteins identified (520 vs. 131 without fractionation), without hindering reproducibility.</p> <p>Conclusions</p> <p>The novelty of this study was to show the advantages and drawbacks of these methods side-to-side. Taking into account the improved detection and potential loss of non-target proteins following extensive immunodepletion, it is concluded that both depletion methods combined with spectral counting may be of interest before further fractionation, when searching for CSF biomarkers. According to the reliable identification and quantitation obtained with APEX algorithm, it may be considered as a cheap and quick alternative to study sample proteomic content.</p

Volatility in the Housing Market: Evidence on Risk and Return in the London Sub-market

The impact of volatility in housing market analysis is reconsidered via examination of the risk-return relationship in the London housing market is examined. In addition to providing the first empirical results for the relationship between risk (as measured by volatility) and returns for this submarket, the analysis offers a more general message to empiricists via a detailed and explicit evaluation of the impact of empirical design decisions upon inferences. In particular, the negative risk-return relationship discussed frequently in the housing market literature is examined and shown to depend upon typically overlooked decisions concerning components of the empirical framework from which statistical inferences are drawn