Interannual variability of the Mid-Atlantic bight cold pool

Author Posting. © American Geophysical Union, 2020. 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: Oceans 125(8), (2020): e2020JC016445, doi:10.1029/2020JC016445.The Mid‐Atlantic Bight (MAB) Cold Pool is a bottom‐trapped, cold (temperature below 10°C) and fresh (practical salinity below 34) water mass that is isolated from the surface by the seasonal thermocline and is located over the midshelf and outer shelf of the MAB. The interannual variability of the Cold Pool with regard to its persistence time, volume, temperature, and seasonal along‐shelf propagation is investigated based on a long‐term (1958–2007) high‐resolution regional model of the northwest Atlantic Ocean. A Cold Pool Index is defined and computed in order to quantify the strength of the Cold Pool on the interannual timescale. Anomalous strong, weak, and normal years are categorized and compared based on the Cold Pool Index. A detailed quantitative study of the volume‐averaged heat budget of the Cold Pool region (CPR) has been examined on the interannual timescale. Results suggest that the initial temperature and abnormal warming/cooling due to advection are the primary drivers in the interannual variability of the near‐bottom CPR temperature anomaly during stratified seasons. The long persistence of temperature anomalies from winter to summer in the CPR also suggests a potential for seasonal predictability.This work was funded by the National Oceanic and Atmospheric Administration through Awards NOAA‐NA‐15OAR4310133 and NOAA‐NA‐13OAR4830233 and the National Science Foundation Awards OCE‐1049088, OCE‐1419584, and OCE‐0961545.2021-02-0

RegExplainer: Generating Explanations for Graph Neural Networks in Regression Task

Graph regression is a fundamental task and has received increasing attention in a wide range of graph learning tasks. However, the inference process is often not interpretable. Most existing explanation techniques are limited to understanding GNN behaviors in classification tasks. In this work, we seek an explanation to interpret the graph regression models (XAIG-R). We show that existing methods overlook the distribution shifting and continuously ordered decision boundary, which hinders them away from being applied in the regression tasks. To address these challenges, we propose a novel objective based on the information bottleneck theory and introduce a new mix-up framework, which could support various GNNs in a model-agnostic manner. We further present a contrastive learning strategy to tackle the continuously ordered labels in regression task. To empirically verify the effectiveness of the proposed method, we introduce three benchmark datasets and a real-life dataset for evaluation. Extensive experiments show the effectiveness of the proposed method in interpreting GNN models in regression tasks

Ocean model-based covariates improve a marine fish stock assessment when observations are limited

The productivity of many fish populations is influenced by the environment, but developing environment-linked stock assessments remain challenging and current management of most commercial species assumes that stock productivity is time-invariant. In the Northeast United States, previous studies suggest that the recruitment of Southern New England-Mid Atlantic yellowtail flounder is closely related to the strength of the Cold Pool, a seasonally formed cold water mass on the continental shelf. Here, we developed three new indices that enhance the characterization of Cold Pool interannual variations using bottom temperature from a regional hindcast ocean model and a global ocean data assimilated hindcast. We associated these new indices to yellowtail flounder recruitment in a state–space, age-structured stock assessment framework using the Woods Hole Assessment Model. We demonstrate that incorporating Cold Pool effects on yellowtail flounder recruitment reduces the retrospective patterns and may improve the predictive skill of recruitment and, to a lesser extent, spawning stock biomass. We also show that the performance of the assessment models that incorporated ocean model-based indices is improved compared to the model using only the observation-based index. Instead of relying on limited subsurface observations, using validated ocean model products as environmental covariates in stock assessments may both improve predictions and facilitate operationalization.publishedVersio

Long-term SST variability on the Northwest Atlantic continental shelf and slope

Author Posting. © American Geophysical Union, 2020. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Geophysical Research Letters 47(1), (2020): e2019GL085455, doi:10.1029/2019GL085455.The meridional coherence, connectivity, and regional inhomogeneity in long‐term sea surface temperature (SST) variability over the Northwest Atlantic continental shelf and slope from 1982–2018 are investigated using observational data sets. A meridionally concurrent large SST warming trend is identified as the dominant signal over the length of the continental shelf and slope between Cape Hatteras in North Carolina and Cape Chidley, Newfoundland and Labrador, Canada. The linear trends are 0.37 ± 0.06 and 0.39 ± 0.06 °C/decade for the shelf and slope regions, respectively. These meridionally averaged SST time series over the shelf and slope are consistent with each other and across multiple longer observational data sets with records dating back to 1900. The coherence between the long‐term meridionally averaged time series over the shelf and slope and basin‐wide averaged SST in the North Atlantic implies approximately two thirds of the warming trend during 1982–2018 may be attributed to natural climate variability and the rest to externally forced change including anthropogenic warming.We are grateful to the Editor Dr. Kathleen Donohue and two anonymous reviewers. This work was supported by NOAA's Climate Program Office's Modeling, Analysis, Predictions, and Projections (MAPP) program (NA19OAR4320074). We acknowledge our participation in MAPP's Marine Prediction Task Force. The data of NOAA OISST used in this study are available at NOAA Earth System Research Laboratory (https://www.esrl.noaa.gov/psd/data/gridded/data.noaa.oisst.v2.highres.html). The HadISST data set is available at Met Office, Hadley Centre (https://www.metoffice.gov.uk/hadobs/hadisst/). The COBE SST and NOAA ERSST data sets are available at NOAA Earth System Research Laboratory's Physical Sciences Division (https://www.esrl.noaa.gov/psd/data/gridded/data.cobe.html; https://www.esrl.noaa.gov/psd/data/gridded/data.noaa.ersst.v5.html). The near‐surface air temperature is available at Global Historical Climatology Network‐Monthly Database (https://www.ncdc.noaa.gov/data‐access/land‐based‐station‐data/land‐based‐datasets/global‐historical‐climatology‐network‐monthly‐version‐4). The data of SSH are available at Copernicus Marine Environment Monitoring Service (http://marine.copernicus.eu/services‐portfolio/access‐to‐products/?option=com_csw&view=details&product_id=SEALEVEL_GLO_PHY_ L4_REP_OBSERVATIONS_008_047).2020-07-0

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

Role of air-sea heat flux on the transformation of Atlantic Water encircling the Nordic Seas

This study reveals that air-sea heat exchange plays differing roles in the transformation of Atlantic Water along the two northward-flowing warm currents in the Nordic Seas, which needs to be considered to understand high-latitude response to climate change

Mixed layer depth climatology over the northeast US continental shelf (1993-2018)

© The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Cai, C., Kwon, Y.-O., Chen, Z., & Fratantoni, P. Mixed layer depth climatology over the northeast US continental shelf (1993-2018). Continental Shelf Research, 231, (2021): 104611 https://doi.org/10.1016/j.csr.2021.104611.The Northeast U.S. (NEUS) continental shelf has experienced rapid warming in recent decades. Over the NEUS continental shelf, the circulation and annual cycle of heating and cooling lead to local variability of water properties. The mixed layer depth (MLD) is a key factor that determines the amount of upper ocean warming. A detailed description of the MLD, particularly its seasonal cycle and spatial patterns, has not been developed for the NEUS continental shelf. We compute the MLD using an observational dataset from the Northeast Fisheries Science Center hydrographic monitoring program. The MLD exhibits clear seasonal cycles across five eco-regions on the NEUS continental shelf, with maxima in January–March and minima in July or August. The seasonal cycle is largest in the western Gulf of Maine (71.9 ± 24.4 m), and smallest in the southern Mid-Atlantic Bight (34.0 ± 7.3 m). Spatial variations are seasonally dependent, with greatest homogeneity in summer. Interannual variability dominates long-term linear trends in most regions and seasons. To evaluate the sensitivity of our results, we compare the MLDs calculated using a 0.03 kg/m3 density threshold with those using a 0.2 °C temperature threshold. Temperature-based MLDs are generally consistent with density-based MLDs, although a small number of temperature-based MLDs are biased deep compared to density-based MLDs particularly in spring and fall. Finally, we compare observational MLDs to the MLDs from a high-resolution ocean reanalysis GLORYS12V1. While the mean values of GLORYS12V1 MLDs compare well with the observed MLDs, their interannual variability are not highly correlated, particularly in summer. These results can be a starting point for future studies on the drivers of temporal and spatial MLD variability on the NEUS continental shelf.The authors gratefully acknowledge the support from the NOAA Modeling, Analysis, Predictions, and Projections (MAPP) Program (NA17OAR4310111) and Climate Variability and Predictability Program (NA20OAR4310482). Cassia Cai acknowledges the Woods Hole Oceanographic Institution Summer Student Fellowship program for participation, the Northwestern University (NU) Earth and Planetary Science Independent Study for supporting the writing of this manuscript, and the NU Climate Change Research Group for providing some of the technical tools to conduct analysis

Miniaturized Thermal-Assisted Purge-and-Trap Technique Coupling with Surface-Enhanced Raman Scattering for Trace Analysis of Complex Samples

It still remains a great challenge for quantification of trace analytes in complex samples by surface-enhanced Raman scattering (SERS) technique due to potential matrix influence or weak SERS responses of analytes. In this work, a miniaturized thermal-assisted purge-and-trap (MTAPT) device was designed and developed to eliminate matrix influence coupled with derivatization method before SERS analysis. The design of MTAPT chamber was optimized based on quantitative calculation of its dead volume by computational fluid dynamics simulation. The small straight chamber was selected as an optimized design with a recovery of 96.1% for formaldehyde. The practical feasibility of MTAPT was validated based on four real analytical applications including phenthiol in industrial water, formaldehyde in flour, sulfion in wastewater, and methanol in industrial alcohol. The results showed that SERS responses of all analytes dramatically increased by eliminating sample matrices after MTAPT process. Phenthiol, formaldehyde, sulfion, and methanol in real samples could be accurately quantified with recoveries of 80.9–110.0%, and the analytical results were validated by corresponding standard methods. The time consumption of MTAPT-SERS for real sample analysis including sample preparation and determination was within 16 min. It is highly expected that the combination of MTAPT technique with portable SERS instrument can greatly expand the range of SERS analysis. The proposed MTAPT-SERS method has high potential for on-site analysis of complex samples