Mixed layer depth climatology over the northeast U.S. continental shelf (1993–2018)

Cai, Cassia; Kwon, Young‐Oh; Fratantoni, Paula S.

doi:10.1016/j.csr.2021.104611

Cited by 10 publications

(5 citation statements)

References 33 publications

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“…In addition, it should be noted that silicate supply might not be the only reason for the correlation between dSSW and the spring bloom magnitude. The distinct hydrographic feature of dSSW (cooler and fresher than the Slope Water) might also influence the spring bloom magnitude by modulating overturning and vertical mixing patterns in the GoM (Christensen and Pringle 2012;Cai et al 2021). Thus, future work should focus more on the synergistic effects of dSSW-related nutrient and hydrographic conditions on spring bloom dynamics.…”

Section: Discussionmentioning

confidence: 99%

Remote silicate supply regulates spring phytoplankton bloom magnitude in the Gulf of Maine

Zang

Liu

et al. 2022

Limnol Oceanogr Letters

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The spring diatom bloom in the Gulf of Maine (GoM) plays an important role in fueling the marine ecosystem and nutrient cycling, whereas the mechanism regulating its magnitude is less understood. We employed an artificial neural network method to identify the spring blooms from satellite images and reconstructed the spring bloom magnitude with strong interannual variability. This study provides the first evidence that the spring bloom magnitude in the central GoM is associated with the inflow of the silicate-rich deep Scotian Shelf Water due to strong silicate limitation. The results suggest that silicate limits the magnitude of spring bloom, and monitoring the proportion of deep Scotian Shelf Water in the Northeast Channel in winter and early spring can help forecast the spring bloom magnitude in the GoM.

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Section: Discussionmentioning

confidence: 99%

Remote silicate supply regulates spring phytoplankton bloom magnitude in the Gulf of Maine

Zang

Liu

et al. 2022

Limnol Oceanogr Letters

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“…These results are further supported by Chen et al 35 , who found that GLORYS bottom temperature is highly consistent with NEUS observations on seasonal and interannual timescales. Further, Cai et al 50 show that GLORYS accurately reproduces the seasonal cycle and interannual variability of MLD along the Northeast US continental shelf and Amaya et al 40 show that GLORYS realistically depicts coastally trapped waves propagating up the US west coast, from Baja to the Gulf of Alaska. Using a 0.25v ersion of GLORYS, Chi et al 51 finds that this reanalysis stands out among other eddy-permitting products in its ability to reproduce the mean state and variability of the Gulf Stream along the Southeast US coastline.…”

Section: Justification For Using Glorysmentioning

confidence: 99%

Bottom marine heatwaves along the continental shelves of North America

et al. 2023

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Recently, there has been substantial effort to understand the fundamental characteristics of warm ocean temperature extremes—known as marine heatwaves (MHWs). However, MHW research has primarily focused on the surface signature of these events. While surface MHWs (SMHW) can have dramatic impacts on marine ecosystems, extreme warming along the seafloor can also have significant biological outcomes. In this study, we use a high-resolution (~8 km) ocean reanalysis to broadly assess bottom marine heatwaves (BMHW) along the continental shelves of North America. We find that BMHW intensity and duration varies strongly with bottom depth, with typical intensities ranging from ~0.5 °C–3 °C. Further, BMHWs can be more intense and persist longer than SMHWs. While BMHWs and SMHWs often co-occur, BMHWs can also exist without a SMHW. Deeper regions in which the mixed layer does not typically reach the seafloor exhibit less synchronicity between BMHWs and SMHWs.

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“…This dataset has undergone rigorous validation against in situ observations and other sea surface temperature (SST) and salinity products, demonstrating robust consistency with independent data sources (e.g., Guinehut et al, 2012;Jean-Michel et al, 2021;Gasparin et al, 2023). Its applicability spans oceanographic and climate research domains (e.g., Xie et al, 2016;Cai et al, 2021;Karnauskas, 2022;Mondal et al, 2023). Accessible from the Copernicus Marine Environment Monitoring Services CMEMS data archive, the dataset covers the temporal span from 1993 to the present.…”

Section: Data Sourcementioning

confidence: 85%

Sea Level Variability and Predictions Using Artificial Neural Networks and Machine Learning Techniques in the Gulf of Guinea

Ayinde,

YU,

2023

Preprint

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The rising sea level due to climate change poses a critical threat, particularly affecting vulnerable low-lying coastal areas such as the Gulf of Guinea (GoG). This impact necessitates precise sea level prediction models to guide planning and mitigation efforts for safeguarding coastal communities and ecosystems. This study presents a comprehensive analysis of mean sea level anomaly (MSLA) trends in the GoG between 1993 and 2020. The assessment covers three distinct periods (1993–2002, 2003–2012, and 2013–2020) and investigates connections between interannual sea level variability and large-scale oceanic and atmospheric forcings. Additionally, the performance of artificial neural networks (LSTM and MLPR) and machine learning techniques (MLR, GBM, and RFR) is evaluated to optimize sea level predictions. The findings reveal a consistent rise in MSLA linear trends across the basin, particularly pronounced in the north, with a total linear trend of 88 mm/year over the entire period. The highest decadal trend (38.7 mm/year) emerged during 2013–2020, and the most substantial percentage increment (100%) occurred in 2003–2012. Spatial variation in decadal sea-level trends was influenced by subbasin physical forcings. Strong interannual signals in the spatial sea level distribution were identified, linked to large-scale oceanic and atmospheric phenomena. Seasonal variations in sea level trends are attributed to seasonal changes in the forcing factors. Model evaluation indicates RFR and GBR as accurate methods, reproducing interannual sea level patterns with 97% and 96% accuracy, respectively. These findings contribute essential insights for effective coastal management and climate adaptation strategies in the GoG.

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Mixed layer depth climatology over the northeast U.S. continental shelf (1993–2018)

Cited by 10 publications

References 33 publications

Remote silicate supply regulates spring phytoplankton bloom magnitude in the Gulf of Maine

Remote silicate supply regulates spring phytoplankton bloom magnitude in the Gulf of Maine

Bottom marine heatwaves along the continental shelves of North America

Sea Level Variability and Predictions Using Artificial Neural Networks and Machine Learning Techniques in the Gulf of Guinea

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