scholarly journals FFNN-LSCE: A two-step neural network model for the reconstruction of surface ocean pCO<sub>2</sub> over the Global Ocean

2018 ◽  
Author(s):  
Anna Denvil-Sommer ◽  
Marion Gehlen ◽  
Mathieu Vrac ◽  
Carlos Mejia
Keyword(s):  
Neural Network ◽  
Indian Ocean ◽  
Southern Oscillation ◽  
Mixed Layer Depth ◽  
Regional Scale ◽  
Sea Surface ◽  
Global Ocean ◽  
Co2 Fluxes ◽  
Surface Ocean ◽  
Data Coverage

Abstract. A new Feed-Forward Neural Network (FFNN) model is presented to reconstruct surface ocean partial pressure of carbon dioxide (pCO2) over the global ocean. The model consists of two steps: (1) reconstruction of pCO2 climatology and (2) reconstruction of pCO2 anomalies with respect to the climatology. For the first step, a gridded climatology was used as the target, along with sea surface salinity and temperature (SSS and SST), sea surface height (SSH), chlorophyll a (Chl), mixed layer depth (MLD), as well as latitude and longitude as predictors. For the second step, data from the Surface Ocean CO2 Atlas (SOCAT) provided the target. The same set of predictors was used during step 2 augmented by their anomalies. During each step, the FFNN model reconstructs the non-linear relations between pCO2 and the ocean predictors. It provides monthly surface ocean pCO2 distributions on a 1º x 1º grid for the period 2001–2016. Global ocean pCO2 was reconstructed with a satisfying accuracy compared to independent observational data from SOCAT. However, errors are larger in regions with poor data coverage (e.g. Indian Ocean, Southern Ocean, subpolar Pacific). The model captured the strong interannual variability of surface ocean pCO2 with reasonable skills over the Equatorial Pacific associated with ENSO (El Niño Southern Oscillation). Our model was compared to three pCO2 mapping methods that participated in the Surface Ocean pCO2 Mapping intercomparison (SOCOM) initiative. We found a good agreement in seasonal and interannual variabilty between the models over the global ocean. However, important differences still exist at the regional scale, especially in the Southern hemisphere and in particular, the Southern Pacific and the Indian Ocean, as these regions suffer from poor data-coverage. Large regional uncertainties in reconstructed surface ocean pCO2 and sea-air CO2 fluxes have a strong influence on global estimates of CO2 fluxes and trends.

scholarly journals LSCE-FFNN-v1: a two-step neural network model for the reconstruction of surface ocean <i>p</i>CO<sub>2</sub> over the global ocean

2019 ◽  
Vol 12 (5) ◽  
pp. 2091-2105 ◽  
Author(s):  
Anna Denvil-Sommer ◽  
Marion Gehlen ◽  
Mathieu Vrac ◽  
Carlos Mejia
Keyword(s):  
Neural Network ◽  
Indian Ocean ◽  
Interannual Variability ◽  
Southern Oscillation ◽  
Sea Surface ◽  
Global Ocean ◽  
Co2 Fluxes ◽  
Surface Ocean ◽  
The Indian Ocean ◽  
Data Coverage

Abstract. A new feed-forward neural network (FFNN) model is presented to reconstruct surface ocean partial pressure of carbon dioxide (pCO2) over the global ocean. The model consists of two steps: (1) the reconstruction of pCO2 climatology, and (2) the reconstruction of pCO2 anomalies with respect to the climatology. For the first step, a gridded climatology was used as the target, along with sea surface salinity (SSS), sea surface temperature (SST), sea surface height (SSH), chlorophyll a (Chl a), mixed layer depth (MLD), as well as latitude and longitude as predictors. For the second step, data from the Surface Ocean CO2 Atlas (SOCAT) provided the target. The same set of predictors was used during step (2) augmented by their anomalies. During each step, the FFNN model reconstructs the nonlinear relationships between pCO2 and the ocean predictors. It provides monthly surface ocean pCO2 distributions on a 1∘×1∘ grid for the period from 2001 to 2016. Global ocean pCO2 was reconstructed with satisfying accuracy compared with independent observational data from SOCAT. However, errors were larger in regions with poor data coverage (e.g., the Indian Ocean, the Southern Ocean and the subpolar Pacific). The model captured the strong interannual variability of surface ocean pCO2 with reasonable skill over the equatorial Pacific associated with ENSO (the El Niño–Southern Oscillation). Our model was compared to three pCO2 mapping methods that participated in the Surface Ocean pCO2 Mapping intercomparison (SOCOM) initiative. We found a good agreement in seasonal and interannual variability between the models over the global ocean. However, important differences still exist at the regional scale, especially in the Southern Hemisphere and, in particular, in the southern Pacific and the Indian Ocean, as these regions suffer from poor data coverage. Large regional uncertainties in reconstructed surface ocean pCO2 and sea–air CO2 fluxes have a strong influence on global estimates of CO2 fluxes and trends.

scholarly journals Observed increase in springtime surface partial pressure of CO<sub>2</sub> in the east equatorial Indian Ocean during 1962–2012

2014 ◽  
Vol 11 (1) ◽  
pp. 521-549
Author(s):  
L. Xue ◽  
W. Yu ◽  
H. Wang ◽  
L. Feng ◽  
Q. Wei ◽  
...  
Keyword(s):  
Indian Ocean ◽  
Partial Pressure ◽  
Mixed Layer ◽  
Mixed Layer Depth ◽  
Equatorial Indian Ocean ◽  
Sea Surface ◽  
Atmospheric Co2 ◽  
Global Ocean ◽  
Layer Depth ◽  
Co2 Sink

Abstract. Rapidly rising atmospheric CO2 and global warming may have been impacting the ocean, and, in contrast, the response of surface CO2 partial pressure (pCO2) in the equatorial Indian Ocean is poorly understood. In this study, we attempted to evaluate the variation of springtime sea surface pCO2 in the east equatorial Indian Ocean (5° N to 5° S along 90° E and 95° E, EIO), which is relatively better occupied, using data collected in May 2012, together with the historical data since 1962 (LDEO_Database_V2012). Results showed that sea surface pCO2 in the investigation area increased from ~308 μatm in April 1963, through ~373 μatm in May 1999, to ~387μatm in May 2012, with a mean increase rate of ~1.7μatm yr−1. Given that the EIO during the study period was almost always a CO2 source to the atmosphere, it was obvious that the observed increase of sea surface pCO2 with time in this region was not due to the local uptake of CO2 via air–sea exchange, although quickly increasing atmospheric CO2 had the potential to increase seawater pCO2. Further, we checked the effects of variations in sea surface temperature, salinity, mixed layer depth and chlorophyll a (as a proxy of biological production) on surface pCO2. We found surface ocean warming partially contributed to sea surface pCO2 increase, whereas the effects of salinity, mixed layer depth, and biological activity were not significant. The pCO2 increase in the equatorial waters (CO2 source to the atmosphere) was probably due to the transport of carbon accumulated in the CO2 sink region (to the atmosphere) towards the CO2 source region on a basin scale via ocean circulation. Additionally, our study showed that more and more release of CO2 from the ocean to the atmosphere and big pH reduction (0.07 pH units) in the past 50 yr (from 1963 to 2012) may have occurred in the EIO. It also demonstrated that ocean acidification may have taken place in the global ocean, not just limited to the CO2 sink region.

Interannual variability of the Eastern Indian Ocean with focus on the Ningaloo Niño and negative Indian Ocean Dipole event in 2010/2011

2020 ◽  
Author(s):  
Svenja Ryan ◽  
Caroline Ummenhofer ◽  
Glen Gawarkiewicz ◽  
Patrick Wagner ◽  
Markus Scheinert ◽  
...  
Keyword(s):  
Indian Ocean ◽  
Indian Ocean Dipole ◽  
General Circulation ◽  
Large Scale ◽  
Southern Oscillation ◽  
Mixed Layer Depth ◽  
Eastern Indian Ocean ◽  
Global Ocean ◽  
Relative Role ◽  
Study Region

&lt;p&gt;The dominant mode of sea surface temperature (SST) variability in the southeast Indian Ocean off the coast of Western Australia is called Ningaloo Ni&amp;#241;o/Ni&amp;#241;a. An unprecedented Ningaloo Ni&amp;#241;o, or marine heatwave, occurred during the austral summer of 2010/2011 with mean SSTs at 3&amp;#176;C above the long-term mean and had drastic impacts on the ecosystem. This event was attributed to a combination of an anomalous strong Leeuwin Current and high local air-sea heat fluxes. A number of local and remote forcing mechanisms have been investigated in recent years, however, little is known about the depth-structure of these ocean extremes and their general connections to large-scale ocean interannual to decadal variability. Using a suite of simulations with a high-resolution global Ocean General Circulation Model from 1958-2016, we investigate eastern Indian Ocean variability with focus on Ningaloo Ni&amp;#241;o and corresponding cold Ningaloo Ni&amp;#241;a events. In particular, we are interested in the impacts of large-scale ocean and climate variability, such as the Indonesian Throughflow, El Ni&amp;#241;o - Southern Oscillation and the Indian Ocean Dipole (IOD), on the study region. Spatial composites reveal large-scale surface and subsurface anomalies that extend from the western Pacific across the Indonesian Archipelago into the tropical eastern Indian Ocean. In particular, strong anomalies in temperature, salinity and mixed layer depth are found to the west of Sumatra and Java, a region that is generally strongly impacted by the IOD. We therefore investigate the connection with Ningaloo Ni&amp;#241;o/Ni&amp;#241;a events, at surface and subsurface, with a focus on 2010/2011 where a strong negative IOD event occurred prior to the unprecedented Ningaloo Ni&amp;#241;o. Furthermore, we find that major heatwaves in 2000 and 2011 are associated with pronounced fresh anomalies. Sensitivity experiments allow us to assess the relative role of buoyancy and wind-forcing as drivers of the observed patterns. Our work can provide valuable contributions for advancing the understanding of Ningaloo Ni&amp;#241;o/Ni&amp;#241;a drivers from surface to depth and regional to large scales.&lt;/p&gt;

Implications of a regional-scale process (the Lakshadweep low) on the mesozooplankton community structure of the Arabian Sea

2019 ◽  
Vol 70 (3) ◽  
pp. 345 ◽  
Author(s):  
K. K. Karati ◽  
G. Vineetha ◽  
T. V. Raveendran ◽  
P. K. Dineshkumar ◽  
K. R. Muraleedharan ◽  
...  
Keyword(s):  
Physical Process ◽  
Arabian Sea ◽  
Mixed Layer Depth ◽  
Regional Scale ◽  
Zooplankton Community ◽  
Ocean Basin ◽  
Zooplankton Biomass ◽  
Global Ocean ◽  
Mesozooplankton Community ◽  
Tropical Ocean

The Arabian Sea, a major tropical ocean basin in the northern Indian Ocean, is one of the most productive regions in the global ocean. Although the classical Arabian Sea ‘paradox’ describes the geographical and seasonal invariability in zooplankton biomass in this region, the effect of the Lakshadweep low (LL), a regional-scale physical process, on the zooplankton community has not yet been evaluated. The LL, characterised by low sea surface height and originating around the vicinity of the Lakshadweep islands during the mid-summer monsoon, is unique to the Arabian Sea. The present study investigated the effect of the LL on the zooplankton community. The LL clearly had a positive effect, with enhanced biomass and abundance in the mixed-layer depth of the LL region. Copepods and chaetognaths formed the dominant taxa, exhibiting strong affinity towards the physical process. Of the 67 copepod species observed, small copepods belonging to the families Paracalanidae, Clausocalanidae, Calanidae, Oncaeidae and Corycaeidae dominated the LL region. Phytoplankton biomass (chlorophyll-a) was the primary determinant influencing the higher preponderance of the copepod community in this region.

scholarly journals Spiciness Anomalies in the Upper South Indian Ocean

2018 ◽  
Vol 48 (9) ◽  
pp. 2081-2101 ◽  
Author(s):  
Motoki Nagura ◽  
Shinya Kouketsu
Keyword(s):  
Indian Ocean ◽  
Southern Oscillation ◽  
Mixed Layer Depth ◽  
Surface Heat ◽  
Surface Fluxes ◽  
Sea Surface Salinity ◽  
South Indian Ocean ◽  
South Indian ◽  
Generation Region ◽  
Upper South

AbstractThis study investigates an isopycnal temperature/salinity T/S, or spiciness, anomaly in the upper south Indian Ocean for the period from 2004 to 2015 using observations and reanalyses. Spiciness anomalies at about 15°S on 24–26σθ are focused on, whose standard deviation is about 0.1 psu in salinity and 0.25°C in temperature, and they have a contribution to isobaric temperature variability comparable to thermocline heave. A plausible generation region of these anomalies is the southeastern Indian Ocean, where the 25σθ surface outcrops in southern winter, and the anticyclonic subtropical gyre advects subducted water equatorward. Unlike the Pacific and Atlantic, spiciness anomalies in the upper south Indian Ocean are not T/S changes in mode water, and meridional variations in SST and sea surface salinity in their generation region are not density compensating. It is possible that this peculiarity is owing to freshwater originating from the Indonesian Seas. The production of spiciness anomalies is estimated from surface heat and freshwater fluxes and the surface T/S relationship in the outcrop region, based on several assumptions including the dominance of surface fluxes in the surface T/S budget and effective mixed layer depth proposed by Deser et al. The result agrees well with isopycnal salinity anomalies at the outcrop line, which indicates that spiciness anomalies are generated by local surface fluxes. It is suggested that the Ningaloo Niño and El Niño–Southern Oscillation lead to interannual variability in surface heat flux in the southeastern Indian Ocean and contribute to the generation of spiciness anomalies.

scholarly journals Global ocean dimethyl sulfide climatology estimated from observations and an artificial neural network

2020 ◽  
Author(s):  
Wei-Lei Wang ◽  
Guisheng Song ◽  
François Primeau ◽  
Eric S. Saltzman ◽  
Thomas G. Bell ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Solar Radiation ◽  
Mixed Layer ◽  
Dimethyl Sulfide ◽  
Mixed Layer Depth ◽  
Environmental Parameters ◽  
Global Scale ◽  
Global Ocean ◽  
Layer Depth

Abstract. Marine dimethyl sulfide (DMS) is important to climate due to the ability of DMS to alter Earth's radiation budget. However, a knowledge of the global-scale distribution, seasonal variability, and sea-to-air flux of DMS is needed in order to understand the factors controlling surface ocean DMS and its impact on climate. Here we examine the use of an artificial neural network (ANN) to extrapolate available DMS measurements to the global ocean and produce a global climatology with monthly temporal resolution. A global database of 57 810 ship-based DMS measurements in surface waters was used along with a suite of environmental parameters consisting of lat-lon coordinates, time-of-day, time-of-year, solar radiation, mixed layer depth, sea surface temperature, salinity, nitrate, phosphate, silicate, and oxygen. Linear regressions of DMS against the environmental parameters show that on a global scale mixed layer depth and solar radiation are the strongest predictors of DMS, however, they capture 14 % and 12 % of the raw DMS data variance, respectively. The multi-linear regression can capture more (∼29 %) of the raw data variance, but strongly underestimates high DMS concentrations. In contrast, the ANN captures ~61 % of the raw data variance in our database. Like prior climatologies our results show a strong seasonal cycle in DMS concentration and sea-to-air flux. The highest concentrations (fluxes) occur in the high-latitude oceans during the summer. We estimate a lower global sea-to-air DMS flux (17.90 &amp;pm; 0.34 Tg S yr−1) than the prior estimate based on a map interpolation method when the same gas transfer velocity parameterization is used.

scholarly journals High-Resolution Sea Surface Temperature and Salinity in the Global Ocean from Raw Satellite Data

2019 ◽  
Vol 11 (19) ◽  
pp. 2191 ◽  
Author(s):  
Encarni Medina-Lopez ◽  
Leonardo Ureña-Fuentes
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Satellite Data ◽  
Google Earth ◽  
Sea Surface ◽  
Global Ocean ◽  
The Neural Network ◽  
Wide Range ◽  
Sentinel 2

The aim of this work is to obtain high-resolution values of sea surface salinity (SSS) and temperature (SST) in the global ocean by using raw satellite data (i.e., without any band data pre-processing or atmospheric correction). Sentinel-2 Level 1-C Top of Atmosphere (TOA) reflectance data is used to obtain accurate SSS and SST information. A deep neural network is built to link the band information with in situ data from different buoys, vessels, drifters, and other platforms around the world. The neural network used in this paper includes shortcuts, providing an improved performance compared with the equivalent feed-forward architecture. The in situ information used as input for the network has been obtained from the Copernicus Marine In situ Service. Sentinel-2 platform-centred band data has been processed using Google Earth Engine in areas of 100 m × 100 m. Accurate salinity values are estimated for the first time independently of temperature. Salinity results rely only on direct satellite observations, although it presented a clear dependency on temperature ranges. Results show the neural network has good interpolation and extrapolation capabilities. Test results present correlation coefficients of 82 % and 84 % for salinity and temperature, respectively. The most common error for both SST and SSS is 0.4 ∘ C and 0 . 4 PSU. The sensitivity analysis shows that outliers are present in areas where the number of observations is very low. The network is finally applied over a complete Sentinel-2 tile, presenting sensible patterns for river-sea interaction, as well as seasonal variations. The methodology presented here is relevant for detailed coastal and oceanographic applications, reducing the time for data pre-processing, and it is applicable to a wide range of satellites, as the information is directly obtained from TOA data.

scholarly journals The Years of El Niño, La Niña, and Interactions with the Tropical Indian Ocean

2007 ◽  
Vol 20 (13) ◽  
pp. 2872-2880 ◽  
Author(s):  
Gary Meyers ◽  
Peter McIntosh ◽  
Lidia Pigot ◽  
Mike Pook
Keyword(s):  
Indian Ocean ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
Sea Surface ◽  
El Nino Southern Oscillation ◽  
La Nina ◽  
The Indian Ocean

Abstract The Indian Ocean zonal dipole is a mode of variability in sea surface temperature that seriously affects the climate of many nations around the Indian Ocean rim, as well as the global climate system. It has been the subject of increasing research, and sometimes of scientific debate concerning its existence/nonexistence and dependence/independence on/from the El Niño–Southern Oscillation, since it was first clearly identified in Nature in 1999. Much of the debate occurred because people did not agree on what years are the El Niño or La Niña years, not to mention the newly defined years of the positive or negative dipole. A method that identifies when the positive or negative extrema of the El Niño–Southern Oscillation and Indian Ocean dipole occur is proposed, and this method is used to classify each year from 1876 to 1999. The method is statistical in nature, but has a strong basis on the oceanic physical mechanisms that control the variability of the near-equatorial Indo-Pacific basin. Early in the study it was found that some years could not be clearly classified due to strong decadal variation; these years also must be recognized, along with the reason for their ambiguity. The sensitivity of the classification of years is tested by calculating composite maps of the Indo-Pacific sea surface temperature anomaly and the probability of below median Australian rainfall for different categories of the El Niño–Indian Ocean relationship.

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 Contributions of Indian Ocean and Monsoon Biases to the Excessive Biennial ENSO in CCSM3

2009 ◽  
Vol 22 (7) ◽  
pp. 1850-1858 ◽  
Author(s):  
Jin-Yi Yu ◽  
Fengpeng Sun ◽  
Hsun-Ying Kao
Keyword(s):  
Indian Ocean ◽  
Southern Oscillation ◽  
Surface Wind ◽  
Tropical Indian Ocean ◽  
Global Ocean ◽  
Climate System Model ◽  
Wind Pattern ◽  
Enso Variability ◽  
Monsoon Variability ◽  
Ocean Atmosphere

Abstract The Community Climate System Model, version 3 (CCSM3), is known to produce many aspects of El Niño–Southern Oscillation (ENSO) realistically, but the simulated ENSO exhibits an overly strong biennial periodicity. Hypotheses on the cause of this excessive biennial tendency have thus far focused primarily on the model’s biases within the tropical Pacific. This study conducts CCSM3 experiments to show that the model’s biases in simulating the Indian Ocean mean sea surface temperatures (SSTs) and the Indian and Australian monsoon variability also contribute to the biennial ENSO tendency. Two CCSM3 simulations are contrasted: a control run that includes global ocean–atmosphere coupling and an experiment in which the air–sea coupling in the tropical Indian Ocean is turned off by replacing simulated SSTs with an observed monthly climatology. The decoupling experiment removes CCSM3’s warm bias in the tropical Indian Ocean and reduces the biennial variability in Indian and Australian monsoons by about 40% and 60%, respectively. The excessive biennial ENSO is found to reduce dramatically by about 75% in the decoupled experiment. It is shown that the biennial monsoon variability in CCSM3 excites an anomalous surface wind pattern in the western Pacific that projects well into the wind pattern associated with the onset phase of the simulated biennial ENSO. Therefore, the biennial monsoon variability is very effective in exciting biennial ENSO variability in CCSM3. The warm SST bias in the tropical Indian Ocean also increases ENSO variability by inducing stronger mean surface easterlies along the equatorial Pacific, which strengthen the Pacific ocean–atmosphere coupling and enhance the ENSO intensity.

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