Saba
Vincent S.
Saba
Vincent S.
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ArticleObservational needs supporting marine ecosystems modeling and forecasting: from the global ocean to regional and coastal systems(Frontiers Media, 2019-10-15) Capotondi, Antonietta ; Jacox, Michael ; Bowler, Chris ; Kavanaugh, Maria T. ; Lehodey, Patrick ; Barrie, Daniel ; Brodie, Stephanie ; Chaffron, Samuel ; Cheng, Wei ; Dias, Daniela F. ; Eveillard, Damien ; Guidi, Lionel ; Iudicone, Daniele ; Lovenduski, Nicole S. ; Nye, Janet A. ; Ortiz, Ivonne ; Pirhalla, Douglas ; Pozo Buil, Mercedes ; Saba, Vincent S. ; Sheridan, Scott ; Siedlecki, Samantha A. ; Subramanian, Aneesh C. ; de Vargas, Colomban ; Di Lorenzo, Emanuele ; Doney, Scott C. ; Hermann, Albert J. ; Joyce, Terrence M. ; Merrifield, Mark ; Miller, Arthur J. ; Not, Fabrice ; Pesant, StephaneMany coastal areas host rich marine ecosystems and are also centers of economic activities, including fishing, shipping and recreation. Due to the socioeconomic and ecological importance of these areas, predicting relevant indicators of the ecosystem state on sub-seasonal to interannual timescales is gaining increasing attention. Depending on the application, forecasts may be sought for variables and indicators spanning physics (e.g., sea level, temperature, currents), chemistry (e.g., nutrients, oxygen, pH), and biology (from viruses to top predators). Many components of the marine ecosystem are known to be influenced by leading modes of climate variability, which provide a physical basis for predictability. However, prediction capabilities remain limited by the lack of a clear understanding of the physical and biological processes involved, as well as by insufficient observations for forecast initialization and verification. The situation is further complicated by the influence of climate change on ocean conditions along coastal areas, including sea level rise, increased stratification, and shoaling of oxygen minimum zones. Observations are thus vital to all aspects of marine forecasting: statistical and/or dynamical model development, forecast initialization, and forecast validation, each of which has different observational requirements, which may be also specific to the study region. Here, we use examples from United States (U.S.) coastal applications to identify and describe the key requirements for an observational network that is needed to facilitate improved process understanding, as well as for sustaining operational ecosystem forecasting. We also describe new holistic observational approaches, e.g., approaches based on acoustics, inspired by Tara Oceans or by landscape ecology, which have the potential to support and expand ecosystem modeling and forecasting activities by bridging global and local observations.
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ArticleChallenges of modeling depth-integrated marine primary productivity over multiple decades : a case study at BATS and HOT(American Geophysical Union, 2010-09-15) Saba, Vincent S. ; Friedrichs, Marjorie A. M. ; Carr, Mary-Elena ; Antoine, David ; Armstrong, Robert A. ; Asanuma, Ichio ; Aumont, Olivier ; Bates, Nicholas R. ; Behrenfeld, Michael J. ; Bennington, Val ; Bopp, Laurent ; Bruggeman, Jorn ; Buitenhuis, Erik T. ; Church, Matthew J. ; Ciotti, Aurea M. ; Doney, Scott C. ; Dowell, Mark ; Dunne, John P. ; Dutkiewicz, Stephanie ; Gregg, Watson ; Hoepffner, Nicolas ; Hyde, Kimberly J. W. ; Ishizaka, Joji ; Kameda, Takahiko ; Karl, David M. ; Lima, Ivan D. ; Lomas, Michael W. ; Marra, John F. ; McKinley, Galen A. ; Melin, Frederic ; Moore, J. Keith ; Morel, Andre ; O'Reilly, John ; Salihoglu, Baris ; Scardi, Michele ; Smyth, Tim J. ; Tang, Shilin ; Tjiputra, Jerry ; Uitz, Julia ; Vichi, Marcello ; Waters, Kirk ; Westberry, Toby K. ; Yool, AndrewThe performance of 36 models (22 ocean color models and 14 biogeochemical ocean circulation models (BOGCMs)) that estimate depth-integrated marine net primary productivity (NPP) was assessed by comparing their output to in situ 14C data at the Bermuda Atlantic Time series Study (BATS) and the Hawaii Ocean Time series (HOT) over nearly two decades. Specifically, skill was assessed based on the models' ability to estimate the observed mean, variability, and trends of NPP. At both sites, more than 90% of the models underestimated mean NPP, with the average bias of the BOGCMs being nearly twice that of the ocean color models. However, the difference in overall skill between the best BOGCM and the best ocean color model at each site was not significant. Between 1989 and 2007, in situ NPP at BATS and HOT increased by an average of nearly 2% per year and was positively correlated to the North Pacific Gyre Oscillation index. The majority of ocean color models produced in situ NPP trends that were closer to the observed trends when chlorophyll-a was derived from high-performance liquid chromatography (HPLC), rather than fluorometric or SeaWiFS data. However, this was a function of time such that average trend magnitude was more accurately estimated over longer time periods. Among BOGCMs, only two individual models successfully produced an increasing NPP trend (one model at each site). We caution against the use of models to assess multiannual changes in NPP over short time periods. Ocean color model estimates of NPP trends could improve if more high quality HPLC chlorophyll-a time series were available.
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PreprintAssessing the uncertainties of model estimates of primary productivity in the tropical Pacific Ocean( 2008-03) Friedrichs, Marjorie A. M. ; Carr, Mary-Elena ; Barber, Richard T. ; Scardi, Michele ; Antoine, David ; Armstrong, Robert A. ; Asanuma, Ichio ; Behrenfeld, Michael J. ; Buitenhuis, Erik T. ; Chai, Fei ; Christian, James R. ; Ciotti, Aurea M. ; Doney, Scott C. ; Dowell, Mark ; Dunne, John P. ; Gentili, Bernard ; Gregg, Watson ; Hoepffner, Nicolas ; Ishizaka, Joji ; Kameda, Takahiko ; Lima, Ivan D. ; Marra, John F. ; Melin, Frederic ; Moore, J. Keith ; Morel, Andre ; O'Malley, Robert T. ; O'Reilly, Jay ; Saba, Vincent S. ; Schmeltz, Marjorie ; Smyth, Tim J. ; Tjiputra, Jerry ; Waters, Kirk ; Westberry, Toby K. ; Winguth, ArneDepth-integrated primary productivity (PP) estimates obtained from satellite ocean color based models (SatPPMs) and those generated from biogeochemical ocean general circulation models (BOGCMs) represent a key resource for biogeochemical and ecological studies at global as well as regional scales. Calibration and validation of these PP models are not straightforward, however, and comparative studies show large differences between model estimates. The goal of this paper is to compare PP estimates obtained from 30 different models (21 SatPPMs and 9 BOGCMs) to a tropical Pacific PP database consisting of ~1000 14C measurements spanning more than a decade (1983- 1996). Primary findings include: skill varied significantly between models, but performance was not a function of model complexity or type (i.e. SatPPM vs. BOGCM); nearly all models underestimated the observed variance of PP, specifically yielding too few low PP (< 0.2 gC m-2d-2) values; more than half of the total root-mean-squared model-data differences associated with the satellite-based PP models might be accounted for by uncertainties in the input variables and/or the PP data; and the tropical Pacific database captures a broad scale shift from low biomass-normalized productivity in the 1980s to higher biomass-normalized productivity in the 1990s, which was not successfully captured by any of the models. This latter result suggests that interdecadal and global changes will be a significant challenge for both SatPPMs and BOGCMs. Finally, average root-mean-squared differences between in situ PP data on the equator at 140°W and PP estimates from the satellite-based productivity models were 58% lower than analogous values computed in a previous PP model comparison six years ago. The success of these types of comparison exercises is illustrated by the continual modification and improvement of the participating models and the resulting increase in model skill.
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ArticleSeasonal prediction of bottom temperature on the Northeast U.S. Continental Shelf(American Geophysical Union, 2021-05-03) Chen, Zhuomin ; Kwon, Young-Oh ; Chen, Ke ; Fratantoni, Paula S. ; Gawarkiewicz, Glen G. ; Joyce, Terrence M. ; Miller, Timothy J. ; Nye, Janet A. ; Saba, Vincent S. ; Stock, Brian C.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.
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ArticleSpring bloom dynamics and zooplankton biomass response on the US Northeast Continental Shelf(Elsevier, 2015-04-07) Friedland, Kevin D. ; Leaf, Robert T. ; Kane, Joe ; Tommasi, Desiree ; Asch, Rebecca G. ; Rebuck, Nathan D. ; Ji, Rubao ; Large, Scott I. ; Stock, Charles A. ; Saba, Vincent S.The spring phytoplankton bloom on the US Northeast Continental Shelf is a feature of the ecosystem production cycle that varies annually in timing, spatial extent, and magnitude. To quantify this variability, we analyzed remotely-sensed ocean color data at two spatial scales, one based on ecologically defined sub-units of the ecosystem (production units) and the other on a regular grid (0.5°). Five units were defined: Gulf of Maine East and West, Georges Bank, and Middle Atlantic Bight North and South. The units averaged 47×103 km2 in size. The initiation and termination of the spring bloom were determined using change-point analysis with constraints on what was identified as a bloom based on climatological bloom patterns. A discrete spring bloom was detected in most years over much of the western Gulf of Maine production unit. However, bloom frequency declined in the eastern Gulf of Maine and transitioned to frequencies as low as 50% along the southern flank of the Georges Bank production unit. Detectable spring blooms were episodic in the Middle Atlantic Bight production units. In the western Gulf of Maine, bloom duration was inversely related to bloom start day; thus, early blooms tended to be longer lasting and larger magnitude blooms. We view this as a phenological mismatch between bloom timing and the “top-down” grazing pressure that terminates a bloom. Estimates of secondary production were available from plankton surveys that provided spring indices of zooplankton biovolume. Winter chlorophyll biomass had little effect on spring zooplankton biovolume, whereas spring chlorophyll biomass had mixed effects on biovolume. There was evidence of a “bottom up” response seen on Georges Bank where spring zooplankton biovolume was positively correlated with the concentration of chlorophyll. However, in the western Gulf of Maine, biovolume was uncorrelated with chlorophyll concentration, but was positively correlated with bloom start and negatively correlated with magnitude. This observation is consistent with both a “top-down” mechanism of control of the bloom and a “bottom-up” effect of bloom timing on zooplankton grazing. Our inability to form a consistent model of these relationships across adjacent systems underscores the need for further research.
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ArticleAn evaluation of eight global ocean reanalyses for the Northeast U.S. continental shelf(Elsevier, 2023-09-22) Carolina Castillo-Trujillo, Alma ; Kwon, Young-oh ; Fratantoni, Paula S. ; Chen, Ke ; Seo, Hyodae ; Alexander, Michael A. ; Saba, Vincent S.The Northeast U.S. continental Shelf (NES) extending from the Gulf of Maine to Cape Hatteras, is a dynamic region supporting some of the most commercially valuable fisheries in the world. This study aims to provide a systematic assessment of eight widely used, intermediate-to-high spatial resolution global ocean reanalysis products (CFSR, ECCO, ORAS, SODA, BRAN, GLORYS, GOFS3.0, and GOFS3.1) against available in situ and satellite ocean observations. In situ observations include water level from tide gauges, and temperature and salinity from various sources including shipboard hydrographic data, and moorings on the NES. Overall, the coarser resolution products exhibit limited skill in the coastal environment, with the high-resolution products better representing the temperature and salinity on the NES. Common biases are found in all reanalyses and in some regions within the NES; for example, biases in temperature and salinity are larger in the southern Mid-Atlantic Bight than in the rest of the NES. There is no single reanalysis that performs well across all parameters in all regions within the NES, but GLORYS and BRAN stand out for their superior performance across the largest number of metrics, outperforming other products in 22 and 25 of the 65 metrics examined, respectively. SODA is the top performer among the coarser resolution products (CFSR, ECCO, ORAS and SODA). The Gulf Stream and local bathymetry are critical factors leading to differences between the reanalyses. Conditions in summer are less well represented than in winter. In particular, the Mid-Atlantic Bight Cold Pool is not reproduced in four (CFSR, ECCO, ORAS, BRAN) of the eight reanalyses.