scholarly journals Predictability of Seawater DMS During the North Atlantic Aerosol and Marine Ecosystem Study (NAAMES)

2021 ◽  
Vol 7 ◽  
Author(s):  
Thomas G. Bell ◽  
Jack G. Porter ◽  
Wei-Lei Wang ◽  
Michael J. Lawler ◽  
Emmanuel Boss ◽  
...  
Keyword(s):  
Neural Network ◽  
North Atlantic ◽  
Spatial Scales ◽  
Mixed Layer Depth ◽  
Marine Ecosystem ◽  
Seawater Temperature ◽  
Physical Factors ◽  
Substantial Impact ◽  
The North ◽  
The North Atlantic

This work presents an overview of a unique set of surface ocean dimethylsulfide (DMS) measurements from four shipboard field campaigns conducted during the North Atlantic Aerosol and Marine Ecosystem Study (NAAMES) project. Variations in surface seawater DMS are discussed in relation to biological and physical observations. Results are considered at a range of timescales (seasons to days) and spatial scales (regional to sub-mesoscale). Elevated DMS concentrations are generally associated with greater biological productivity, although chlorophyll a (Chl) only explains a small fraction of the DMS variability (15%). Physical factors that determine the location of oceanic temperature fronts and depth of vertical mixing have an important influence on seawater DMS concentrations during all seasons. The interplay of biomass and physics influences DMS concentrations at regional/seasonal scales and at smaller spatial and shorter temporal scales. Seawater DMS measurements are compared with the global seawater DMS climatology and predictions made using a recently published algorithm and by a neural network model. The climatology is successful at capturing the seasonal progression in average seawater DMS, but does not reproduce the shorter spatial/temporal scale variability. The input terms common to the algorithm and neural network approaches are biological (Chl) and physical (mixed layer depth, photosynthetically active radiation, seawater temperature). Both models predict the seasonal North Atlantic average seawater DMS trends better than the climatology. However, DMS concentrations tend to be under-predicted and the episodic occurrence of higher DMS concentrations is poorly predicted. The choice of climatological seawater DMS product makes a substantial impact on the estimated DMS flux into the North Atlantic atmosphere. These results suggest that additional input terms are needed to improve the predictive capability of current state-of-the-art approaches to estimating seawater DMS.

scholarly journals Predictability of seawater DMS during the North Atlantic Aerosol and Marine Ecosystem Study (NAAMES)

2021 ◽  
Author(s):  
Thomas Bell ◽  
Jack Porter ◽  
Wei-Lei Wang ◽  
Michael Lawler ◽  
Faith Hoyle ◽  
...  
Keyword(s):  
Neural Network ◽  
North Atlantic ◽  
Mixed Layer Depth ◽  
Marine Ecosystem ◽  
Seawater Temperature ◽  
Substantial Impact ◽  
The North ◽  
Current State ◽  
The North Atlantic ◽  
Network Approaches

<p>Surface ocean dimethylsulfide (DMS) was measured during four shipboard field campaigns conducted during the North Atlantic Aerosol and Marine Ecosystem Study (NAAMES).  Variations in surface seawater DMS are discussed in relation to biological and physical observations. The interplay of biomass and physics influences DMS concentrations at regional/seasonal scales and at smaller spatial and shorter temporal scales. Observations are compared with the best-available climatological predictions of seawater DMS, including output from an empirical algorithm and a neural network model. The input terms common to the algorithm and neural network approaches are biological (chlorophyll) and physical (mixed layer depth, photosynthetically active radiation, seawater temperature). DMS concentrations tend to be under-predicted and the episodic occurrence of higher DMS concentrations is poorly predicted. The choice of climatological seawater DMS product makes a substantial impact on the estimated DMS flux into the North Atlantic atmosphere. These results suggest that additional input terms are needed to improve the predictive capability of current state-of-the-art approaches to estimating seawater DMS.</p>

scholarly journals Estimating the monthly <i>p</i>CO<sub>2</sub> distribution in the North Atlantic using a self-organizing neural network

2009 ◽  
Vol 6 (8) ◽  
pp. 1405-1421 ◽  
Author(s):  
M. Telszewski ◽  
A. Chazottes ◽  
U. Schuster ◽  
A. J. Watson ◽  
C. Moulin ◽  
...  
Keyword(s):  
Neural Network ◽  
North Atlantic ◽  
Mixed Layer Depth ◽  
Statistical Modelling ◽  
Self Organizing Map ◽  
The North ◽  
Linear Relationships ◽  
The North Atlantic ◽  
Self Organizing

Abstract. Here we present monthly, basin-wide maps of the partial pressure of carbon dioxide (pCO2) for the North Atlantic on a 1° latitude by 1° longitude grid for years 2004 through 2006 inclusive. The maps have been computed using a neural network technique which reconstructs the non-linear relationships between three biogeochemical parameters and marine pCO2. A self organizing map (SOM) neural network has been trained using 389 000 triplets of the SeaWiFS-MODIS chlorophyll-a concentration, the NCEP/NCAR reanalysis sea surface temperature, and the FOAM mixed layer depth. The trained SOM was labelled with 137 000 underway pCO2 measurements collected in situ during 2004, 2005 and 2006 in the North Atlantic, spanning the range of 208 to 437 μatm. The root mean square error (RMSE) of the neural network fit to the data is 11.6 μatm, which equals to just above 3 per cent of an average pCO2 value in the in situ dataset. The seasonal pCO2 cycle as well as estimates of the interannual variability in the major biogeochemical provinces are presented and discussed. High resolution combined with basin-wide coverage makes the maps a useful tool for several applications such as the monitoring of basin-wide air-sea CO2 fluxes or improvement of seasonal and interannual marine CO2 cycles in future model predictions. The method itself is a valuable alternative to traditional statistical modelling techniques used in geosciences.

scholarly journals Estimating mixed layer nitrate in the North Atlantic Ocean

2010 ◽  
Vol 7 (3) ◽  
pp. 795-807 ◽  
Author(s):  
T. Steinhoff ◽  
T. Friedrich ◽  
S. E. Hartman ◽  
A. Oschlies ◽  
D. W. R. Wallace ◽  
...  
Keyword(s):  
Neural Network ◽  
Atlantic Ocean ◽  
Observational Data ◽  
Mixed Layer ◽  
North Atlantic ◽  
North Atlantic Ocean ◽  
Mixed Layer Depth ◽  
Model Data ◽  
The North ◽  
The North Atlantic

Abstract. Here we present an equation for the estimation of nitrate in surface waters of the North Atlantic Ocean (40° N to 52° N, 10° W to 60° W). The equation was derived by multiple linear regression (MLR) from nitrate, sea surface temperature (SST) observational data and model mixed layer depth (MLD) data. The observational data were taken from merchant vessels that have crossed the North Atlantic on a regular basis in 2002/2003 and from 2005 to the present. It is important to find a robust and realistic estimate of MLD because the deepening of the mixed layer is crucial for nitrate supply to the surface. We compared model data from two models (FOAM and Mercator) with MLD derived from float data (using various criteria). The Mercator model gives a MLD estimate that is close to the MLD derived from floats. MLR was established using SST, MLD from Mercator, time and latitude as predictors. Additionally a neural network was trained with the same dataset and the results were validated against both model data as a "ground truth" and an independent observational dataset. This validation produced RMS errors of the same order for MLR and the neural network approach. We conclude that it is possible to estimate nitrate concentrations with an uncertainty of ±1.4 μmol L−1 in the North Atlantic.

scholarly journals Estimating mixed layer nitrate in the North Atlantic Ocean

2009 ◽  
Vol 6 (5) ◽  
pp. 8851-8881
Author(s):  
T. Steinhoff ◽  
T. Friedrich ◽  
S. E. Hartman ◽  
A. Oschlies ◽  
D. W. R. Wallace ◽  
...  
Keyword(s):  
Neural Network ◽  
Atlantic Ocean ◽  
Observational Data ◽  
Mixed Layer ◽  
North Atlantic ◽  
North Atlantic Ocean ◽  
Mixed Layer Depth ◽  
Model Data ◽  
The North ◽  
The North Atlantic

Abstract. Here we present an equation for the estimation of nitrate in surface waters of the North Atlantic Ocean (40° N to 52° N, 10° W to 60° W). The equation was derived by multiple linear regression (MLR) from nitrate, sea surface temperature (SST) observational data and model mixed layer depth (MLD) data. The observational data were taken from merchant vessels that have crossed the North Atlantic on a regular basis in 2002/2003 and from 2005 to present. It is important to find a robust and realistic esitmate of MLD because the deepening of the mixed layer is crucial for nitrate supply to the surface. We compared model data from two models (FOAM and Mercator) with MLD derived from float data (using various criteria). The Mercator model gives a MLD estimate that is close to the MLD derived from floats. MLR was established using SST, MLD from Mercator, time and latitude as predictors. Additionally a neural network was trained with the same dataset and the results were validated against both model data as a "ground truth" and an independent observational dataset. This validation produced RMS errors of the same order for MLR and the neural network approach. We conclude that it is possible to estimate nitrate concentrations with an uncertainty of ±1.5 μmol L−1 in the North Atlantic.

scholarly journals Estimating the monthly <i>p</i>CO<sub>2</sub> distribution in the North Atlantic using a self-organizing neural network

2009 ◽  
Vol 6 (2) ◽  
pp. 3373-3414 ◽  
Author(s):  
M. Telszewski ◽  
A. Chazottes ◽  
U. Schuster ◽  
A. J. Watson ◽  
C. Moulin ◽  
...  
Keyword(s):  
Neural Network ◽  
North Atlantic ◽  
Mixed Layer Depth ◽  
Statistical Modelling ◽  
Spatial And Temporal Variability ◽  
Self Organizing Map ◽  
The North ◽  
The North Atlantic ◽  
Self Organizing

Abstract. Here we present monthly, basin-wide maps of the partial pressure of carbon dioxide (pCO2) for the North Atlantic on a 1° latitude by 1° longitude grid for years 2004 through 2006 inclusive, constructed using a neural network technique which reconstructs the non-linear relationships between 3 biogeochemical parameters and marine pCO2. A self organizing map (SOM) neural network has been trained using the SeaWiFS-MODIS chlorophyll a concentration, the NCEP/NCAR reanalysis sea surface temperature, and the FOAM mixed layer depth. 389 000 such triplets were used. The trained SOM was labelled with 137 000 underway pCO2 measurements collected in situ during 2004, 2005 and 2006 in the North Atlantic, which span the range of 208 and 437 μatm. The root mean square (RMS) deviation of the neural network fits from the data is 11.55 μatm, which equals to just above 3 per cent of an average pCO2 value in the in situ dataset. The seasonal pCO2 cycle as well as the interannual variability estimates in the major biogeochemical provinces is presented and spatial and temporal variability of the estimated fields is discussed. High resolution combined with basin-wide cover makes the maps a useful tool for several applications such as monitoring of basin-wide air-sea CO2 fluxes or improvement of seasonal and interannual marine CO2 cycles in future model predictions. The method itself is a valuable alternative to traditional statistical modelling techniques used in geosciences.

scholarly journals Space-Time Sea Surface pCO2 Estimation in the North Atlantic Based on CatBoost

2021 ◽  
Vol 13 (14) ◽  
pp. 2805
Author(s):  
Hongwei Sun ◽  
Junyu He ◽  
Yihui Chen ◽  
Boyu Zhao
Keyword(s):  
North Atlantic ◽  
Function Analysis ◽  
Biological Activities ◽  
Mixed Layer Depth ◽  
Sea Surface ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Bias Error ◽  
The North ◽  
The North Atlantic

Sea surface partial pressure of CO2 (pCO2) is a critical parameter in the quantification of air–sea CO2 flux, which plays an important role in calculating the global carbon budget and ocean acidification. In this study, we used chlorophyll-a concentration (Chla), sea surface temperature (SST), dissolved and particulate detrital matter absorption coefficient (Adg), the diffuse attenuation coefficient of downwelling irradiance at 490 nm (Kd) and mixed layer depth (MLD) as input data for retrieving the sea surface pCO2 in the North Atlantic based on a remote sensing empirical approach with the Categorical Boosting (CatBoost) algorithm. The results showed that the root mean square error (RMSE) is 8.25 μatm, the mean bias error (MAE) is 4.92 μatm and the coefficient of determination (R2) can reach 0.946 in the validation set. Subsequently, the proposed algorithm was applied to the sea surface pCO2 in the North Atlantic Ocean during 2003–2020. It can be found that the North Atlantic sea surface pCO2 has a clear trend with latitude variations and have strong seasonal changes. Furthermore, through variance analysis and EOF (empirical orthogonal function) analysis, the sea surface pCO2 in this area is mainly affected by sea temperature and salinity, while it can also be influenced by biological activities in some sub-regions.

scholarly journals External forcing mechanisms controlling the North Atlantic coastal upwelling regime during the mid-Holocene

Geology ◽  
2020 ◽  
Author(s):  
Armand Hernández ◽  
Mário Cachão ◽  
Pedro Sousa ◽  
Ricardo M. Trigo ◽  
Jürg Luterbacher ◽  
...  
Keyword(s):  
North Atlantic ◽  
Coastal Upwelling ◽  
Marine Ecosystem ◽  
External Forcing ◽  
The North ◽  
New Findings ◽  
The North Atlantic ◽  
Eastern North Atlantic ◽  
Forcing Mechanisms ◽  
The Impact

Nearshore upwelling along the eastern North Atlantic margin regulates regional marine ecosystem productivity and thus impacts blue economies. While most global circulation models show an increase in the intensity and duration of seasonal upwelling at high latitudes under future human-induced warmer conditions, projections for the North Atlantic are still ambiguous. Due to the low temporal resolution of coastal upwelling records, little is known about the impact of natural forcing mechanisms on upwelling variability. Here, we present a microfossil-based proxy record and modeling simulations for the warmest period of the Holocene (ca. 9–5 ka) to estimate the contribution of the natural variability in North Atlantic upwelling via atmospheric and oceanic dynamics. We found that more frequent high-pressure conditions in the eastern North Atlantic associated with solar activity and orbital parameters triggered upwelling variations at multidecadal and millennial time scales, respectively. Our new findings offer insights into the role of external forcing mechanisms in upwelling changes before the Anthropocene, which must be considered when producing future projections of midlatitude upwelling activity.

Variability of the Oceanic Mixed Layer, 1960–2004

2008 ◽  
Vol 21 (5) ◽  
pp. 1029-1047 ◽  
Author(s):  
James A. Carton ◽  
Semyon A. Grodsky ◽  
Hailong Liu
Keyword(s):  
Mixed Layer ◽  
North Atlantic ◽  
Southern Oscillation ◽  
Mixed Layer Depth ◽  
Global Ocean ◽  
Boreal Summer ◽  
Layer Depth ◽  
The North ◽  
The North Atlantic ◽  
Mixed Layers

Abstract A new monthly uniformly gridded analysis of mixed layer properties based on the World Ocean Atlas 2005 global ocean dataset is used to examine interannual and longer changes in mixed layer properties during the 45-yr period 1960–2004. The analysis reveals substantial variability in the winter–spring depth of the mixed layer in the subtropics and midlatitudes. In the North Pacific an empirical orthogonal function analysis shows a pattern of mixed layer depth variability peaking in the central subtropics. This pattern occurs coincident with intensification of local surface winds and may be responsible for the SST changes associated with the Pacific decadal oscillation. Years with deep winter–spring mixed layers coincide with years in which winter–spring SST is low. In the North Atlantic a pattern of winter–spring mixed layer depth variability occurs that is not so obviously connected to local changes in winds or SST, suggesting that other processes such as advection are more important. Interestingly, at decadal periods the winter–spring mixed layers of both basins show trends, deepening by 10–40 m over the 45-yr period of this analysis. The long-term mixed layer deepening is even stronger (50–100 m) in the North Atlantic subpolar gyre. At tropical latitudes the boreal winter mixed layer varies in phase with the Southern Oscillation index, deepening in the eastern Pacific and shallowing in the western Pacific and eastern Indian Oceans during El Niños. In boreal summer the mixed layer in the Arabian Sea region of the western Indian Ocean varies in response to changes in the strength of the southwest monsoon.

scholarly journals The North Atlantic Aerosol and Marine Ecosystem Study (NAAMES): Science Motive and Mission Overview

2019 ◽  
Vol 6 ◽  
Author(s):  
Michael J. Behrenfeld ◽  
Richard H. Moore ◽  
Chris A. Hostetler ◽  
Jason Graff ◽  
Peter Gaube ◽  
...  
Keyword(s):  
North Atlantic ◽  
Marine Ecosystem ◽  
The North ◽  
The North Atlantic

scholarly journals Satellite-Derived Ocean Thermal Structure for the North Atlantic Hurricane Season

2016 ◽  
Vol 144 (3) ◽  
pp. 877-896 ◽  
Author(s):  
Iam-Fei Pun ◽  
James F. Price ◽  
Steven R. Jayne
Keyword(s):  
Mixed Layer ◽  
North Atlantic ◽  
Thermal Structure ◽  
Estimation Error ◽  
Mixed Layer Depth ◽  
Heat Content ◽  
Height Anomaly ◽  
The North ◽  
Hurricane Season ◽  
The North Atlantic

Abstract This paper describes a new model (method) called Satellite-derived North Atlantic Profiles (SNAP) that seeks to provide a high-resolution, near-real-time ocean thermal field to aid tropical cyclone (TC) forecasting. Using about 139 000 observed temperature profiles, a spatially dependent regression model is developed for the North Atlantic Ocean during hurricane season. A new step introduced in this work is that the daily mixed layer depth is derived from the output of a one-dimensional Price–Weller–Pinkel ocean mixed layer model with time-dependent surface forcing. The accuracy of SNAP is assessed by comparison to 19 076 independent Argo profiles from the hurricane seasons of 2011 and 2013. The rms differences of the SNAP-estimated isotherm depths are found to be 10–25 m for upper thermocline isotherms (29°–19°C), 35–55 m for middle isotherms (18°–7°C), and 60–100 m for lower isotherms (6°–4°C). The primary error sources include uncertainty of sea surface height anomaly (SSHA), high-frequency fluctuations of isotherm depths, salinity effects, and the barotropic component of SSHA. These account for roughly 29%, 25%, 19%, and 10% of the estimation error, respectively. The rms differences of TC-related ocean parameters, upper-ocean heat content, and averaged temperature of the upper 100 m, are ~10 kJ cm−2 and ~0.8°C, respectively, over the North Atlantic basin. These errors are typical also of the open ocean underlying the majority of TC tracks. Errors are somewhat larger over regions of greatest mesoscale variability (i.e., the Gulf Stream and the Loop Current within the Gulf of Mexico).

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