One-dimensional ecosystem model of the equatorial Pacific upwelling system. Part I: model development and silicon and nitrogen cycle

Chai, Fei; Dugdale, Richard C.; Peng, Tsung‐Hung; Wilkerson, Frances P.; Barber, Richard T.

doi:10.1016/s0967-0645(02)00055-3

Cited by 268 publications

(263 citation statements)

References 77 publications

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“…Starting over two decades ago, biogeochemical models began incorporating multiple PT (e.g., Baretta et al, 1995;Le Quéré et al, 2005;Gregg and Casey, 2007) mainly to incorporate their biogeochemical relevance. As a first step models incorporated a "diatom" group given their importance in the silica cycle, but also given their potential important role in carbon export compared to other PT (e.g., Chai et al, 2002). As models became more sophisticated and started to simulate nitrogen biogeochemistry, many added a "diazotroph" class.…”

Section: Approachmentioning

confidence: 99%

Obtaining Phytoplankton Diversity from Ocean Color: A Scientific Roadmap for Future Development

et al. 2017

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To improve our understanding of the role of phytoplankton for marine ecosystems and global biogeochemical cycles, information on the global distribution of major phytoplankton groups is essential. Although algorithms have been developed to assess phytoplankton diversity from space for over two decades, so far the application of these data sets has been limited. This scientific roadmap identifies user needs, summarizes the current state of the art, and pinpoints major gaps in long-term objectives to deliver space-derived phytoplankton diversity data that meets the user requirements. These major gaps in using ocean color to estimate phytoplankton community structure were identified as: (a) the mismatch between satellite, in situ and model data on phytoplankton composition, (b) the lack of quantitative uncertainty estimates provided with satellite data, (c) the spectral limitation of current sensors to enable the full exploitation of backscattered sunlight, and (d) the very limited applicability of satellite algorithms determining phytoplankton composition for regional, especially coastal or inland, waters. Recommendation for actions include but are not limited to: (i) an increased communication and round-robin exercises among and within the related expert groups, (ii) the launching of higher spectrally and spatially resolved sensors, (iii) the development of algorithms that exploit

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Section: Approachmentioning

confidence: 99%

Obtaining Phytoplankton Diversity from Ocean Color: A Scientific Roadmap for Future Development

et al. 2017

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“…It is believed that community growth is limited by insufficient iron in the euphotic zone (Coale et al, 1996;Martin et al, 1994;Wang et al, 2005a;, and diatom growth also by silica Dugdale and Wilkerson, 1998;Chai et al, 2002). To date, most studies of the marine nitrogen cycle have largely focused on the distribution and behavior of nitrate in the equatorial Pacific.…”

Section: Introductionmentioning

confidence: 99%

Spatial and temporal variations in dissolved and particulate organic nitrogen in the equatorial Pacific: biological and physical influences

et al. 2008

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Abstract.To quote Libby and Wheeler (1997), "we have only a cursory knowledge of the distributions of dissolved and particulate organic nitrogen" in the equatorial Pacific. A decade later, we are still in need of spatial and temporal analyses of these organic nitrogen pools. To address this issue, we employ a basin scale physical-biogeochemical model to study the spatial and temporal variations of dissolved organic nitrogen (DON) and particulate organic nitrogen (PON). The model is able to reproduce many observed features of nitrate, ammonium, DON and PON in the central and eastern equatorial Pacific, including the asymmetries of nitrate and ammonium, and the meridional distributions of DON and PON. Modeled DON (5-8 mmol m −3 ) shows small zonal and meridional variations in the mixed layer whereas modeled PON (0.4-1.5 mmol m −3 ) shows considerable spatial variability. While there is a moderate seasonality in both DON and PON in the mixed layer, there is a much weaker interannual variability in DON than in PON. The interannual variability in PON is largely associated with the El Niño/Southern Oscillation (ENSO) phenomenon, showing high values during cold ENSO phase but low values during warm ENSO phase. Overall, DON and PON have significant positive correlations with phytoplankton and zooplankton in the mixed layer, indicting the biological regulation on distribution of organic nitrogen. However, the relationships with phytoplankton and zooplankton are much weaker for DON (r=0.18-0.71) than for PON (r=0.25-0.97). Such a difference is ascribed to a relatively larger degree of physical control (e.g., upwelling of low-organic-N deep waters into the surface) on DON than PON.

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“…The original 10-component CoSiNE model from Chai et al (2002) was modified to include 31 state variables (Figure 1). With these new variables, the model can simulate photosynthesis, inorganic nitrogen assimilation, and pigment synthesis processes separately associated with dynamic carbon-tochlorophyll and carbon-to-nitrogen ratios.…”

Section: Resultsmentioning

confidence: 99%

“…The CoSiNE model (Chai et al, 2002;2003;) was developed originally for the equatorial and Pacific Ocean. During the past several years, we have implemented the CoSiNE model for different regional studies and improved its performance.…”

Section: Security Classification Of: 17 Limitation Of Abstractmentioning

confidence: 99%

Physical-Biological-Optics Model Development and Simulation for the Pacific Ocean and Monterey Bay, California

Chai¹,

Boss²

2012

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LONG-TERM GOALSModeling and predicting ocean optical properties requires linking optical properties with the physical, chemical, and biological processes in the upper ocean. Our long-term goal is to incorporate optical processes into coupled physical-biological models for both open ocean and coastal waters, develop and improve integrated ocean forecasting systems, including prediction of ocean optical properties. OBJECTIVES 1) To improve performance of the coupled physical-biological model, which is based on the Navy Coastal Ocean Model (NCOM) for the California Current System and Regional Ocean Model System (ROMS) for the Pacific Ocean;2) To incorporate optical variables into the improved coupled 3D physical-biological model for the Pacific Ocean and California Current System;3) To evaluate physical-biological-optical models with remote sensing and available in situ observations; 4) To use these variables to drive a radiative transfer model (EcoLight) that simulates and predicts the subsurface light field as well as the ocean optical measurements. APPROACHTo achieve the first objective, we have conducted a series of 3D physical-biological model simulations for the Pacific Ocean and California Current System to test the model performance. By collaborating

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One-dimensional ecosystem model of the equatorial Pacific upwelling system. Part I: model development and silicon and nitrogen cycle

Cited by 268 publications

References 77 publications

Obtaining Phytoplankton Diversity from Ocean Color: A Scientific Roadmap for Future Development

Obtaining Phytoplankton Diversity from Ocean Color: A Scientific Roadmap for Future Development

Spatial and temporal variations in dissolved and particulate organic nitrogen in the equatorial Pacific: biological and physical influences

Physical-Biological-Optics Model Development and Simulation for the Pacific Ocean and Monterey Bay, California

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