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Over vast periods of time, our primitive ocean formed. Water remained a gas until the Earth cooled below 212 degrees Fahrenheit. At this time, about 3.8 billion years ago, the water condensed into rain which filled the basins that we now know as our world ocean.

A coastal ocean dead zone is mainly caused by the flow of excess nutrients humans use on land, such as fertilizer application. In the Northern Gulf of Mexico, anthropogenic nutrients delivered by the Mississippi River annually produce a dead zone as big as the state of New Jersey.

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The researchers from universities in the U.S., Canada, Taiwan, Germany, and Australia set out to determine the evolution of open ocean dead zones before human activity began to impact the ocean, Wang said. In addition, did these dead zones always exist? If so, why?

When oxygen deficient zones expand, denitrification zones also expand, raising the nitrogen-15 to nitrogen-14 ratio of the remaining nitrate, which is then recorded in ocean organisms such as foraminifera through the cycling of nitrogen in the marine ecosystems, according to the report.

The sedimentary records showed the team that the largest open ocean dead zones gradually expanded over the past eight million years, said Wang, who was joined on the project by colleagues from the Max Planck Institute for Chemistry, Princeton University, National Taiwan University, University of Toronto, Texas A&M University, University of Western Australia, and Caltech.

These findings may help better predict the future behavior of open ocean dead zones, according to the report. For example, human activities have been adding more and more nitrogen to the ocean. They can support the need to improve climate and ocean models to better gauge the impact of anthropogenic nitrogen on the deoxygenation processes in the open ocean.

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Is not only to create beautiful swimwear that is of the highest quality and a reliable source for amazing fit and style, but to create awareness and support the efforts to clean our oceans and beaches in order to help save, heal, and preserve the life that gives us life.

Schematic of the Kalnay dynamical rule of the local phase relationship between SST and vorticity. This rule identifies the driver in the local coupling via the sign of vorticity and SST anomalies. (top) If the atmosphere drives the ocean, a cyclonic atmospheric anomaly will induce upwelling of a cold SST anomaly in the oceanic mixed layer driven by Ekman suction, while an anticyclonic circulation anomaly will induce downwelling of warm SST anomalies in the oceanic mixed layer.1 At the same time, the anomalous cyclonic circulation is associated with cloudy skies that reduce insolation of the surface and cool the ocean surface further. Under an anticyclonic circulation, on the other hand, clear skies enhance insolation and warming of the ocean surface. (bottom) When the ocean drives the atmosphere, warm ocean anomalies will drive upward motion in the lower atmosphere by creating a low pressure zone and low-level cyclonic circulation. Cold ocean anomalies will drive downward motion in the lower atmosphere by creating a high pressure zone and low-level air divergence, and hence an anticyclonic circulation. For more details on the dynamical rule see Kalnay et al. (1986), Mo and Kalnay (1991), Pea et al. (2003, 2004), and Ruiz-Barradas et al. (2017). For more details on the atmospheric response to SST anomalies in the tropics see Sobel (2007), and for the extratropics see Kushnir et al. (2002).

(a) Atmosphere-to-ocean and (b) ocean-to-atmosphere predictability. White grid cells indicate that the null hypothesis of no gain in predictability could not be rejected at the 95% significance level.

Due to the physical coupling between atmosphere and ocean, information about the ocean helps to better predict the future of the atmosphere, and in turn, information about the atmosphere helps to better predict the ocean. Here, we investigate the spatial and temporal nature of this predictability: where, for how long, and at what frequencies does the ocean significantly improve prediction of the atmosphere, and vice versa? We apply Granger causality, a statistical test to measure whether a variable improves prediction of another, to local time series of sea surface temperature (SST) and low-level atmospheric variables. We calculate the detailed spatial structure of the atmosphere-to-ocean and ocean-to-atmosphere predictability. We find that the atmosphere improves prediction of the ocean most in the extratropics, especially in regions of large SST gradients. This atmosphere-to-ocean predictability is weaker but longer-lived in the tropics, where it can last for several months in some regions. On the other hand, the ocean improves prediction of the atmosphere most significantly in the tropics, where this predictability lasts for months to over a year. However, we find a robust signature of the ocean on the atmosphere almost everywhere in the extratropics, an influence that has been difficult to demonstrate with model studies. We find that both the atmosphere-to-ocean and ocean-to-atmosphere predictability are maximal at low frequencies, and both are larger in the summer hemisphere. The patterns we observe generally agree with dynamical understanding and the results of the Kalnay dynamical rule, which diagnoses the direction of forcing between the atmosphere and ocean by considering the local phase relationship between simultaneous sea surface temperature and vorticity anomaly signals. We discuss applications to coupled data assimilation.

Climate and its variability are strongly influenced by the ocean, and in particular by SSTs, which also play a key role as a source of potential predictability for climate fluctuations. The large-scale structure of SST anomalies depends not only on large-scale atmospheric circulation and its ensuing heat fluxes but also on heat transport by currents and vertical mixing (Ekman currents and pumping). Ekman pumping is especially energetic at subsynoptic scales (Frankignoul 1985; Deser et al. 2010). The coupling between SST anomalies and the overlying atmospheric circulation varies geographically. It is known that in the extratropics, it is primarily the atmosphere that drives SST rather than vice versa (Frankignoul 1985). This atmosphere-forced variability is an important source of low-frequency variability in the climate system (Hasselmann 1976; Frankignoul and Hasselmann 1977). In modeling studies, Luksch and von Storch (1992), Luksch (1996) found that much of the low-frequency SST variability in the North Pacific and the North Atlantic could be explained by wind anomalies, mainly through anomalous heat fluxes and Ekman transport. Several studies have examined how predictable SST is from atmospheric forcing, including Scott (2003) with an idealized stochastic model. Model results have also shown that tropical SSTs are highly predictable when atmospheric fluxes are prescribed (Shukla and Kinter 2006). 006ab0faaa